diff --git a/.gitignore b/.gitignore
index 671bfc7..9a508b2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -139,4 +139,8 @@ checklink/cookies.txt
 # .gitconfig is now autogenerated
 .gitconfig
 
-conda/
\ No newline at end of file
+conda/
+
+_token
+
+data/
\ No newline at end of file
diff --git a/00_seq.ipynb b/00_seq.ipynb
index 4c871fb..a2c41e9 100644
--- a/00_seq.ipynb
+++ b/00_seq.ipynb
@@ -118,8 +118,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|████████████████████████████████████████████| 2/2 [00:00<00:00, 144.95it/s]\n",
-      "100%|████████████████████████████████████████████| 2/2 [00:00<00:00, 247.74it/s]\n"
+      "100%|██████████| 2/2 [00:00<00:00, 180.02it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "100%|██████████| 2/2 [00:00<00:00, 410.74it/s]\n"
      ]
     }
    ],
@@ -177,7 +184,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████████████████████████████████| 8659/8659 [00:14<00:00, 612.35it/s]\n"
+      "100%|██████████| 8659/8659 [00:15<00:00, 552.53it/s]\n"
      ]
     }
    ],
@@ -203,7 +210,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████████████████████████████████| 7442/7442 [00:12<00:00, 610.30it/s]\n"
+      "100%|██████████| 7442/7442 [00:13<00:00, 548.35it/s]\n"
      ]
     }
    ],
@@ -230,7 +237,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x400 with 1 Axes>"
       ]
@@ -258,9 +265,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "rs3",
+   "display_name": "test_rs3_v6",
    "language": "python",
-   "name": "rs3"
+   "name": "python3"
   }
  },
  "nbformat": 4,
diff --git a/01_targetdata.ipynb b/01_targetdata.ipynb
index ee04bdb..376d264 100644
--- a/01_targetdata.ipynb
+++ b/01_targetdata.ipynb
@@ -36,7 +36,8 @@
     "import warnings\n",
     "import os\n",
     "from scipy import stats\n",
-    "import multiprocessing"
+    "import multiprocessing\n",
+    "import io"
    ]
   },
   {
@@ -147,13 +148,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████████| 1/1 [00:01<00:00,  1.53s/it]\n"
+      "100%|██████████| 1/1 [00:01<00:00,  1.69s/it]\n"
      ]
     }
    ],
    "source": [
     "assert(post_transcript_sequence([\"ENSG00000157764\", \"ENSG00000248378\"],\n",
-    "                                seq_type='genomic')[0]['seq'][:4] == 'CTTC')"
+    "                                seq_type='genomic')[0]['seq'][:4] == 'GTCT')"
    ]
   },
   {
@@ -215,7 +216,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████████| 4/4 [00:10<00:00,  2.52s/it]\n"
+      "100%|██████████| 4/4 [00:02<00:00,  1.54it/s]\n",
+      "/var/folders/n9/c397fj3n5f16khr7tp76tbpc0000gn/T/ipykernel_37465/1237391668.py:23: UserWarning: Unable to find translations for the following transcripts: ENST00000629399, ENST00000284049\n",
+      "  warnings.warn('Unable to find translations for the following transcripts: ' + ', '.join(missing_seqs))\n"
      ]
     },
     {
@@ -243,8 +246,8 @@
        "      <th>Target Total Length</th>\n",
        "      <th>Transcript Base</th>\n",
        "      <th>version</th>\n",
-       "      <th>molecule</th>\n",
        "      <th>desc</th>\n",
+       "      <th>molecule</th>\n",
        "      <th>id</th>\n",
        "      <th>seq</th>\n",
        "      <th>AA len</th>\n",
@@ -257,8 +260,8 @@
        "      <td>834</td>\n",
        "      <td>ENST00000259457</td>\n",
        "      <td>3</td>\n",
-       "      <td>protein</td>\n",
        "      <td>None</td>\n",
+       "      <td>protein</td>\n",
        "      <td>ENSP00000259457</td>\n",
        "      <td>MAAVSVYAPPVGGFSFDNCRRNAVLEADFAKRGYKLPKVRKTGTTI...</td>\n",
        "      <td>277</td>\n",
@@ -269,8 +272,8 @@
        "      <td>1863</td>\n",
        "      <td>ENST00000394249</td>\n",
        "      <td>3</td>\n",
-       "      <td>protein</td>\n",
        "      <td>None</td>\n",
+       "      <td>protein</td>\n",
        "      <td>ENSP00000377793</td>\n",
        "      <td>MRRSEVLAEESIVCLQKALNHLREIWELIGIPEDQRLQRTEVVKKH...</td>\n",
        "      <td>620</td>\n",
@@ -281,8 +284,8 @@
        "      <td>2298</td>\n",
        "      <td>ENST00000361337</td>\n",
        "      <td>2</td>\n",
-       "      <td>protein</td>\n",
        "      <td>None</td>\n",
+       "      <td>protein</td>\n",
        "      <td>ENSP00000354522</td>\n",
        "      <td>MSGDHLHNDSQIEADFRLNDSHKHKDKHKDREHRHKEHKKEKDREK...</td>\n",
        "      <td>765</td>\n",
@@ -293,8 +296,8 @@
        "      <td>267</td>\n",
        "      <td>ENST00000368328</td>\n",
        "      <td>4</td>\n",
-       "      <td>protein</td>\n",
        "      <td>None</td>\n",
+       "      <td>protein</td>\n",
        "      <td>ENSP00000357311</td>\n",
        "      <td>MALSTIVSQRKQIKRKAPRGFLKRVFKRKKPQLRLEKSGDLLVHLN...</td>\n",
        "      <td>88</td>\n",
@@ -305,8 +308,8 @@
        "      <td>783</td>\n",
        "      <td>ENST00000610426</td>\n",
        "      <td>1</td>\n",
-       "      <td>protein</td>\n",
        "      <td>None</td>\n",
+       "      <td>protein</td>\n",
        "      <td>ENSP00000483484</td>\n",
        "      <td>MPQNEYIELHRKRYGYRLDYHEKKRKKESREAHERSKKAKKMIGLK...</td>\n",
        "      <td>260</td>\n",
@@ -324,96 +327,96 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>195</th>\n",
+       "      <th>193</th>\n",
        "      <td>ENST00000339374.11</td>\n",
        "      <td>1371</td>\n",
        "      <td>ENST00000339374</td>\n",
        "      <td>6</td>\n",
-       "      <td>protein</td>\n",
        "      <td>None</td>\n",
+       "      <td>protein</td>\n",
        "      <td>ENSP00000343041</td>\n",
        "      <td>MAAALKCLLTLGRWCPGLGVAPQARALAALVPGVTQVDNKSGFLQK...</td>\n",
        "      <td>456</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>196</th>\n",
+       "      <th>194</th>\n",
        "      <td>ENST00000393047.8</td>\n",
        "      <td>882</td>\n",
        "      <td>ENST00000393047</td>\n",
        "      <td>3</td>\n",
-       "      <td>protein</td>\n",
        "      <td>None</td>\n",
+       "      <td>protein</td>\n",
        "      <td>ENSP00000376767</td>\n",
        "      <td>MSRIPLGKVLLRNVIRHTDAHNKIQEESDMWKIRELEKQMEDAYRG...</td>\n",
        "      <td>293</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>197</th>\n",
+       "      <th>195</th>\n",
        "      <td>ENST00000454402.7</td>\n",
        "      <td>1023</td>\n",
        "      <td>ENST00000454402</td>\n",
        "      <td>2</td>\n",
-       "      <td>protein</td>\n",
        "      <td>None</td>\n",
+       "      <td>protein</td>\n",
        "      <td>ENSP00000408295</td>\n",
        "      <td>METSALKQQEQPAATKIRNLPWVEKYRPQTLNDLISHQDILSTIQK...</td>\n",
        "      <td>340</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>198</th>\n",
+       "      <th>196</th>\n",
        "      <td>ENST00000254998.3</td>\n",
        "      <td>423</td>\n",
        "      <td>ENST00000254998</td>\n",
        "      <td>2</td>\n",
-       "      <td>protein</td>\n",
        "      <td>None</td>\n",
+       "      <td>protein</td>\n",
        "      <td>ENSP00000254998</td>\n",
        "      <td>MASVDFKTYVDQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTATLV...</td>\n",
        "      <td>140</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>199</th>\n",
+       "      <th>197</th>\n",
        "      <td>ENST00000381685.10</td>\n",
        "      <td>2067</td>\n",
        "      <td>ENST00000381685</td>\n",
        "      <td>5</td>\n",
-       "      <td>protein</td>\n",
        "      <td>None</td>\n",
+       "      <td>protein</td>\n",
        "      <td>ENSP00000371101</td>\n",
        "      <td>MQVSSLNEVKIYSLSCGKSLPEWLSDRKKRALQKKDVDVRRRIELI...</td>\n",
        "      <td>688</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>200 rows × 9 columns</p>\n",
+       "<p>198 rows × 9 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "      Target Transcript  Target Total Length  Transcript Base  version  \\\n",
-       "0     ENST00000259457.8                  834  ENST00000259457        3   \n",
-       "1     ENST00000394249.8                 1863  ENST00000394249        3   \n",
-       "2     ENST00000361337.3                 2298  ENST00000361337        2   \n",
-       "3     ENST00000368328.5                  267  ENST00000368328        4   \n",
-       "4     ENST00000610426.5                  783  ENST00000610426        1   \n",
-       "..                  ...                  ...              ...      ...   \n",
-       "195  ENST00000339374.11                 1371  ENST00000339374        6   \n",
-       "196   ENST00000393047.8                  882  ENST00000393047        3   \n",
-       "197   ENST00000454402.7                 1023  ENST00000454402        2   \n",
-       "198   ENST00000254998.3                  423  ENST00000254998        2   \n",
-       "199  ENST00000381685.10                 2067  ENST00000381685        5   \n",
+       "      Target Transcript  Target Total Length  Transcript Base  version  desc  \\\n",
+       "0     ENST00000259457.8                  834  ENST00000259457        3  None   \n",
+       "1     ENST00000394249.8                 1863  ENST00000394249        3  None   \n",
+       "2     ENST00000361337.3                 2298  ENST00000361337        2  None   \n",
+       "3     ENST00000368328.5                  267  ENST00000368328        4  None   \n",
+       "4     ENST00000610426.5                  783  ENST00000610426        1  None   \n",
+       "..                  ...                  ...              ...      ...   ...   \n",
+       "193  ENST00000339374.11                 1371  ENST00000339374        6  None   \n",
+       "194   ENST00000393047.8                  882  ENST00000393047        3  None   \n",
+       "195   ENST00000454402.7                 1023  ENST00000454402        2  None   \n",
+       "196   ENST00000254998.3                  423  ENST00000254998        2  None   \n",
+       "197  ENST00000381685.10                 2067  ENST00000381685        5  None   \n",
        "\n",
-       "    molecule  desc               id  \\\n",
-       "0    protein  None  ENSP00000259457   \n",
-       "1    protein  None  ENSP00000377793   \n",
-       "2    protein  None  ENSP00000354522   \n",
-       "3    protein  None  ENSP00000357311   \n",
-       "4    protein  None  ENSP00000483484   \n",
-       "..       ...   ...              ...   \n",
-       "195  protein  None  ENSP00000343041   \n",
-       "196  protein  None  ENSP00000376767   \n",
-       "197  protein  None  ENSP00000408295   \n",
-       "198  protein  None  ENSP00000254998   \n",
-       "199  protein  None  ENSP00000371101   \n",
+       "    molecule               id  \\\n",
+       "0    protein  ENSP00000259457   \n",
+       "1    protein  ENSP00000377793   \n",
+       "2    protein  ENSP00000354522   \n",
+       "3    protein  ENSP00000357311   \n",
+       "4    protein  ENSP00000483484   \n",
+       "..       ...              ...   \n",
+       "193  protein  ENSP00000343041   \n",
+       "194  protein  ENSP00000376767   \n",
+       "195  protein  ENSP00000408295   \n",
+       "196  protein  ENSP00000254998   \n",
+       "197  protein  ENSP00000371101   \n",
        "\n",
        "                                                   seq  AA len  \n",
        "0    MAAVSVYAPPVGGFSFDNCRRNAVLEADFAKRGYKLPKVRKTGTTI...     277  \n",
@@ -422,13 +425,13 @@
        "3    MALSTIVSQRKQIKRKAPRGFLKRVFKRKKPQLRLEKSGDLLVHLN...      88  \n",
        "4    MPQNEYIELHRKRYGYRLDYHEKKRKKESREAHERSKKAKKMIGLK...     260  \n",
        "..                                                 ...     ...  \n",
-       "195  MAAALKCLLTLGRWCPGLGVAPQARALAALVPGVTQVDNKSGFLQK...     456  \n",
-       "196  MSRIPLGKVLLRNVIRHTDAHNKIQEESDMWKIRELEKQMEDAYRG...     293  \n",
-       "197  METSALKQQEQPAATKIRNLPWVEKYRPQTLNDLISHQDILSTIQK...     340  \n",
-       "198  MASVDFKTYVDQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTATLV...     140  \n",
-       "199  MQVSSLNEVKIYSLSCGKSLPEWLSDRKKRALQKKDVDVRRRIELI...     688  \n",
+       "193  MAAALKCLLTLGRWCPGLGVAPQARALAALVPGVTQVDNKSGFLQK...     456  \n",
+       "194  MSRIPLGKVLLRNVIRHTDAHNKIQEESDMWKIRELEKQMEDAYRG...     293  \n",
+       "195  METSALKQQEQPAATKIRNLPWVEKYRPQTLNDLISHQDILSTIQK...     340  \n",
+       "196  MASVDFKTYVDQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTATLV...     140  \n",
+       "197  MQVSSLNEVKIYSLSCGKSLPEWLSDRKKRALQKKDVDVRRRIELI...     688  \n",
        "\n",
-       "[200 rows x 9 columns]"
+       "[198 rows x 9 columns]"
       ]
      },
      "execution_count": null,
@@ -539,7 +542,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████| 200/200 [00:54<00:00,  3.65it/s]\n"
+      "100%|██████████| 198/198 [00:49<00:00,  4.00it/s]\n"
      ]
     },
     {
@@ -563,113 +566,113 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>description</th>\n",
-       "      <th>hit_start</th>\n",
-       "      <th>id</th>\n",
-       "      <th>seq_region_name</th>\n",
        "      <th>cigar_string</th>\n",
+       "      <th>align_type</th>\n",
+       "      <th>hit_start</th>\n",
+       "      <th>start</th>\n",
+       "      <th>description</th>\n",
        "      <th>hit_end</th>\n",
        "      <th>hseqname</th>\n",
-       "      <th>translation_id</th>\n",
-       "      <th>end</th>\n",
-       "      <th>feature_type</th>\n",
-       "      <th>start</th>\n",
-       "      <th>Transcript Base</th>\n",
-       "      <th>align_type</th>\n",
+       "      <th>seq_region_name</th>\n",
        "      <th>interpro</th>\n",
        "      <th>type</th>\n",
+       "      <th>feature_type</th>\n",
+       "      <th>Transcript Base</th>\n",
+       "      <th>translation_id</th>\n",
+       "      <th>end</th>\n",
+       "      <th>id</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>Nucleophile aminohydrolases, N-terminal</td>\n",
-       "      <td>0</td>\n",
-       "      <td>SSF56235</td>\n",
-       "      <td>ENSP00000259457</td>\n",
        "      <td></td>\n",
-       "      <td>0</td>\n",
-       "      <td>SSF56235</td>\n",
-       "      <td>976188</td>\n",
-       "      <td>236</td>\n",
+       "      <td>None</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Proteasome B-type subunit</td>\n",
+       "      <td>234</td>\n",
+       "      <td>PTHR32194</td>\n",
+       "      <td>ENSP00000259457</td>\n",
+       "      <td>IPR023333</td>\n",
+       "      <td>PANTHER</td>\n",
        "      <td>protein_feature</td>\n",
-       "      <td>6</td>\n",
        "      <td>ENST00000259457</td>\n",
-       "      <td>None</td>\n",
-       "      <td>IPR029055</td>\n",
-       "      <td>SuperFamily</td>\n",
+       "      <td>1109135</td>\n",
+       "      <td>237</td>\n",
+       "      <td>PTHR32194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td></td>\n",
-       "      <td>0</td>\n",
-       "      <td>seg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>44</td>\n",
+       "      <td>Via SIFTS (2025/02/17) UniProt protein Q99436 ...</td>\n",
+       "      <td>220</td>\n",
+       "      <td>4r3o.I</td>\n",
        "      <td>ENSP00000259457</td>\n",
        "      <td></td>\n",
-       "      <td>0</td>\n",
-       "      <td>seg</td>\n",
-       "      <td>976188</td>\n",
-       "      <td>99</td>\n",
+       "      <td>sifts</td>\n",
        "      <td>protein_feature</td>\n",
-       "      <td>88</td>\n",
        "      <td>ENST00000259457</td>\n",
-       "      <td>None</td>\n",
-       "      <td></td>\n",
-       "      <td>Seg</td>\n",
+       "      <td>1109135</td>\n",
+       "      <td>263</td>\n",
+       "      <td>4r3o.I</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>Proteasome subunit beta 7</td>\n",
-       "      <td>23</td>\n",
-       "      <td>PTHR11599:SF42</td>\n",
+       "      <td></td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>44</td>\n",
+       "      <td>Via SIFTS (2025/02/17) UniProt protein Q99436 ...</td>\n",
+       "      <td>220</td>\n",
+       "      <td>4r3o.W</td>\n",
        "      <td>ENSP00000259457</td>\n",
        "      <td></td>\n",
-       "      <td>273</td>\n",
-       "      <td>PTHR11599:SF42</td>\n",
-       "      <td>976188</td>\n",
-       "      <td>273</td>\n",
+       "      <td>sifts</td>\n",
        "      <td>protein_feature</td>\n",
-       "      <td>23</td>\n",
        "      <td>ENST00000259457</td>\n",
-       "      <td>None</td>\n",
-       "      <td>IPR035216</td>\n",
-       "      <td>PANTHER</td>\n",
+       "      <td>1109135</td>\n",
+       "      <td>263</td>\n",
+       "      <td>4r3o.W</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td></td>\n",
-       "      <td>23</td>\n",
-       "      <td>PTHR11599</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>44</td>\n",
+       "      <td>Via SIFTS (2025/02/17) UniProt protein Q99436 ...</td>\n",
+       "      <td>220</td>\n",
+       "      <td>4r67.I</td>\n",
        "      <td>ENSP00000259457</td>\n",
        "      <td></td>\n",
-       "      <td>273</td>\n",
-       "      <td>PTHR11599</td>\n",
-       "      <td>976188</td>\n",
-       "      <td>273</td>\n",
+       "      <td>sifts</td>\n",
        "      <td>protein_feature</td>\n",
-       "      <td>23</td>\n",
        "      <td>ENST00000259457</td>\n",
-       "      <td>None</td>\n",
-       "      <td></td>\n",
-       "      <td>PANTHER</td>\n",
+       "      <td>1109135</td>\n",
+       "      <td>263</td>\n",
+       "      <td>4r67.I</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>Proteasome B-type subunit</td>\n",
-       "      <td>0</td>\n",
-       "      <td>PS51476</td>\n",
+       "      <td></td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>44</td>\n",
+       "      <td>Via SIFTS (2025/02/17) UniProt protein Q99436 ...</td>\n",
+       "      <td>220</td>\n",
+       "      <td>4r67.W</td>\n",
        "      <td>ENSP00000259457</td>\n",
        "      <td></td>\n",
-       "      <td>0</td>\n",
-       "      <td>PS51476</td>\n",
-       "      <td>976188</td>\n",
-       "      <td>221</td>\n",
+       "      <td>sifts</td>\n",
        "      <td>protein_feature</td>\n",
-       "      <td>43</td>\n",
        "      <td>ENST00000259457</td>\n",
-       "      <td>None</td>\n",
-       "      <td>IPR023333</td>\n",
-       "      <td>Prosite_profiles</td>\n",
+       "      <td>1109135</td>\n",
+       "      <td>263</td>\n",
+       "      <td>4r67.W</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -690,154 +693,154 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5275</th>\n",
-       "      <td>WD40 repeat</td>\n",
-       "      <td>1</td>\n",
-       "      <td>SM00320</td>\n",
+       "      <th>7675</th>\n",
+       "      <td></td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>558</td>\n",
+       "      <td>Coil</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Coil</td>\n",
        "      <td>ENSP00000371101</td>\n",
        "      <td></td>\n",
-       "      <td>46</td>\n",
-       "      <td>SM00320</td>\n",
-       "      <td>975385</td>\n",
-       "      <td>81</td>\n",
+       "      <td>ncoils</td>\n",
        "      <td>protein_feature</td>\n",
-       "      <td>42</td>\n",
        "      <td>ENST00000381685</td>\n",
-       "      <td>None</td>\n",
-       "      <td>IPR001680</td>\n",
-       "      <td>Smart</td>\n",
+       "      <td>1303115</td>\n",
+       "      <td>587</td>\n",
+       "      <td>Coil</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5276</th>\n",
-       "      <td>WD40 repeat</td>\n",
-       "      <td>1</td>\n",
-       "      <td>SM00320</td>\n",
+       "      <th>7676</th>\n",
+       "      <td></td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>516</td>\n",
+       "      <td>Coil</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Coil</td>\n",
        "      <td>ENSP00000371101</td>\n",
        "      <td></td>\n",
-       "      <td>46</td>\n",
-       "      <td>SM00320</td>\n",
-       "      <td>975385</td>\n",
-       "      <td>204</td>\n",
+       "      <td>ncoils</td>\n",
        "      <td>protein_feature</td>\n",
-       "      <td>165</td>\n",
        "      <td>ENST00000381685</td>\n",
-       "      <td>None</td>\n",
-       "      <td>IPR001680</td>\n",
-       "      <td>Smart</td>\n",
+       "      <td>1303115</td>\n",
+       "      <td>537</td>\n",
+       "      <td>Coil</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5277</th>\n",
-       "      <td>WD40 repeat</td>\n",
-       "      <td>1</td>\n",
-       "      <td>SM00320</td>\n",
+       "      <th>7677</th>\n",
+       "      <td></td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>426</td>\n",
+       "      <td>Coil</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Coil</td>\n",
        "      <td>ENSP00000371101</td>\n",
        "      <td></td>\n",
-       "      <td>46</td>\n",
-       "      <td>SM00320</td>\n",
-       "      <td>975385</td>\n",
-       "      <td>257</td>\n",
+       "      <td>ncoils</td>\n",
        "      <td>protein_feature</td>\n",
-       "      <td>223</td>\n",
        "      <td>ENST00000381685</td>\n",
-       "      <td>None</td>\n",
-       "      <td>IPR001680</td>\n",
-       "      <td>Smart</td>\n",
+       "      <td>1303115</td>\n",
+       "      <td>446</td>\n",
+       "      <td>Coil</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5278</th>\n",
-       "      <td>WD40 repeat</td>\n",
+       "      <th>7678</th>\n",
+       "      <td></td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
        "      <td>1</td>\n",
-       "      <td>SM00320</td>\n",
+       "      <td>Nucleolar protein 10/Enp2</td>\n",
+       "      <td>655</td>\n",
+       "      <td>PTHR14927</td>\n",
        "      <td>ENSP00000371101</td>\n",
-       "      <td></td>\n",
-       "      <td>46</td>\n",
-       "      <td>SM00320</td>\n",
-       "      <td>975385</td>\n",
-       "      <td>299</td>\n",
+       "      <td>IPR040382</td>\n",
+       "      <td>PANTHER</td>\n",
        "      <td>protein_feature</td>\n",
-       "      <td>260</td>\n",
        "      <td>ENST00000381685</td>\n",
-       "      <td>None</td>\n",
-       "      <td>IPR001680</td>\n",
-       "      <td>Smart</td>\n",
+       "      <td>1303115</td>\n",
+       "      <td>684</td>\n",
+       "      <td>PTHR14927</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5279</th>\n",
-       "      <td>WD40 repeat</td>\n",
-       "      <td>1</td>\n",
-       "      <td>SM00320</td>\n",
-       "      <td>ENSP00000371101</td>\n",
+       "      <th>7679</th>\n",
        "      <td></td>\n",
-       "      <td>46</td>\n",
-       "      <td>SM00320</td>\n",
-       "      <td>975385</td>\n",
-       "      <td>340</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>42</td>\n",
+       "      <td>WD40-repeat-containing domain superfamily</td>\n",
+       "      <td>0</td>\n",
+       "      <td>SSF50978</td>\n",
+       "      <td>ENSP00000371101</td>\n",
+       "      <td>IPR036322</td>\n",
+       "      <td>SuperFamily</td>\n",
        "      <td>protein_feature</td>\n",
-       "      <td>302</td>\n",
        "      <td>ENST00000381685</td>\n",
-       "      <td>None</td>\n",
-       "      <td>IPR001680</td>\n",
-       "      <td>Smart</td>\n",
+       "      <td>1303115</td>\n",
+       "      <td>339</td>\n",
+       "      <td>SSF50978</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5280 rows × 15 columns</p>\n",
+       "<p>7680 rows × 15 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                  description  hit_start              id  \\\n",
-       "0     Nucleophile aminohydrolases, N-terminal          0        SSF56235   \n",
-       "1                                                      0             seg   \n",
-       "2                   Proteasome subunit beta 7         23  PTHR11599:SF42   \n",
-       "3                                                     23       PTHR11599   \n",
-       "4                   Proteasome B-type subunit          0         PS51476   \n",
-       "...                                       ...        ...             ...   \n",
-       "5275                              WD40 repeat          1         SM00320   \n",
-       "5276                              WD40 repeat          1         SM00320   \n",
-       "5277                              WD40 repeat          1         SM00320   \n",
-       "5278                              WD40 repeat          1         SM00320   \n",
-       "5279                              WD40 repeat          1         SM00320   \n",
+       "     cigar_string align_type  hit_start  start  \\\n",
+       "0                       None          3      3   \n",
+       "1                       None          1     44   \n",
+       "2                       None          1     44   \n",
+       "3                       None          1     44   \n",
+       "4                       None          1     44   \n",
+       "...           ...        ...        ...    ...   \n",
+       "7675                    None          0    558   \n",
+       "7676                    None          0    516   \n",
+       "7677                    None          0    426   \n",
+       "7678                    None          1      1   \n",
+       "7679                    None          0     42   \n",
        "\n",
-       "      seq_region_name cigar_string  hit_end        hseqname  translation_id  \\\n",
-       "0     ENSP00000259457                     0        SSF56235          976188   \n",
-       "1     ENSP00000259457                     0             seg          976188   \n",
-       "2     ENSP00000259457                   273  PTHR11599:SF42          976188   \n",
-       "3     ENSP00000259457                   273       PTHR11599          976188   \n",
-       "4     ENSP00000259457                     0         PS51476          976188   \n",
-       "...               ...          ...      ...             ...             ...   \n",
-       "5275  ENSP00000371101                    46         SM00320          975385   \n",
-       "5276  ENSP00000371101                    46         SM00320          975385   \n",
-       "5277  ENSP00000371101                    46         SM00320          975385   \n",
-       "5278  ENSP00000371101                    46         SM00320          975385   \n",
-       "5279  ENSP00000371101                    46         SM00320          975385   \n",
+       "                                            description  hit_end   hseqname  \\\n",
+       "0                             Proteasome B-type subunit      234  PTHR32194   \n",
+       "1     Via SIFTS (2025/02/17) UniProt protein Q99436 ...      220     4r3o.I   \n",
+       "2     Via SIFTS (2025/02/17) UniProt protein Q99436 ...      220     4r3o.W   \n",
+       "3     Via SIFTS (2025/02/17) UniProt protein Q99436 ...      220     4r67.I   \n",
+       "4     Via SIFTS (2025/02/17) UniProt protein Q99436 ...      220     4r67.W   \n",
+       "...                                                 ...      ...        ...   \n",
+       "7675                                               Coil        0       Coil   \n",
+       "7676                                               Coil        0       Coil   \n",
+       "7677                                               Coil        0       Coil   \n",
+       "7678                          Nucleolar protein 10/Enp2      655  PTHR14927   \n",
+       "7679          WD40-repeat-containing domain superfamily        0   SSF50978   \n",
        "\n",
-       "      end     feature_type  start  Transcript Base align_type   interpro  \\\n",
-       "0     236  protein_feature      6  ENST00000259457       None  IPR029055   \n",
-       "1      99  protein_feature     88  ENST00000259457       None              \n",
-       "2     273  protein_feature     23  ENST00000259457       None  IPR035216   \n",
-       "3     273  protein_feature     23  ENST00000259457       None              \n",
-       "4     221  protein_feature     43  ENST00000259457       None  IPR023333   \n",
-       "...   ...              ...    ...              ...        ...        ...   \n",
-       "5275   81  protein_feature     42  ENST00000381685       None  IPR001680   \n",
-       "5276  204  protein_feature    165  ENST00000381685       None  IPR001680   \n",
-       "5277  257  protein_feature    223  ENST00000381685       None  IPR001680   \n",
-       "5278  299  protein_feature    260  ENST00000381685       None  IPR001680   \n",
-       "5279  340  protein_feature    302  ENST00000381685       None  IPR001680   \n",
+       "      seq_region_name   interpro         type     feature_type  \\\n",
+       "0     ENSP00000259457  IPR023333      PANTHER  protein_feature   \n",
+       "1     ENSP00000259457                   sifts  protein_feature   \n",
+       "2     ENSP00000259457                   sifts  protein_feature   \n",
+       "3     ENSP00000259457                   sifts  protein_feature   \n",
+       "4     ENSP00000259457                   sifts  protein_feature   \n",
+       "...               ...        ...          ...              ...   \n",
+       "7675  ENSP00000371101                  ncoils  protein_feature   \n",
+       "7676  ENSP00000371101                  ncoils  protein_feature   \n",
+       "7677  ENSP00000371101                  ncoils  protein_feature   \n",
+       "7678  ENSP00000371101  IPR040382      PANTHER  protein_feature   \n",
+       "7679  ENSP00000371101  IPR036322  SuperFamily  protein_feature   \n",
        "\n",
-       "                  type  \n",
-       "0          SuperFamily  \n",
-       "1                  Seg  \n",
-       "2              PANTHER  \n",
-       "3              PANTHER  \n",
-       "4     Prosite_profiles  \n",
-       "...                ...  \n",
-       "5275             Smart  \n",
-       "5276             Smart  \n",
-       "5277             Smart  \n",
-       "5278             Smart  \n",
-       "5279             Smart  \n",
+       "      Transcript Base  translation_id  end         id  \n",
+       "0     ENST00000259457         1109135  237  PTHR32194  \n",
+       "1     ENST00000259457         1109135  263     4r3o.I  \n",
+       "2     ENST00000259457         1109135  263     4r3o.W  \n",
+       "3     ENST00000259457         1109135  263     4r67.I  \n",
+       "4     ENST00000259457         1109135  263     4r67.W  \n",
+       "...               ...             ...  ...        ...  \n",
+       "7675  ENST00000381685         1303115  587       Coil  \n",
+       "7676  ENST00000381685         1303115  537       Coil  \n",
+       "7677  ENST00000381685         1303115  446       Coil  \n",
+       "7678  ENST00000381685         1303115  684  PTHR14927  \n",
+       "7679  ENST00000381685         1303115  339   SSF50978  \n",
        "\n",
-       "[5280 rows x 15 columns]"
+       "[7680 rows x 15 columns]"
       ]
      },
      "execution_count": null,
@@ -914,7 +917,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████████| 4/4 [00:02<00:00,  1.51it/s]\n"
+      "100%|██████████| 4/4 [00:01<00:00,  2.11it/s]\n",
+      "/var/folders/n9/c397fj3n5f16khr7tp76tbpc0000gn/T/ipykernel_37465/1237391668.py:23: UserWarning: Unable to find translations for the following transcripts: ENST00000629399, ENST00000284049\n",
+      "  warnings.warn('Unable to find translations for the following transcripts: ' + ', '.join(missing_seqs))\n"
      ]
     },
     {
@@ -928,14 +933,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████| 200/200 [00:46<00:00,  4.31it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Creating new directory ./data/target_data/\n"
+      "100%|██████████| 198/198 [00:50<00:00,  3.95it/s]\n"
      ]
     }
    ],
@@ -1024,7 +1022,7 @@
     "    }\n",
     "    results = requests.get(api_url, data=params)\n",
     "    if results.ok:\n",
-    "        value_df = (pd.DataFrame([pd.Series(x) for x in pd.read_json(results.content.decode('utf8'))[chrom].values])\n",
+    "        value_df = (pd.DataFrame([pd.Series(x) for x in pd.read_json(io.StringIO(results.content.decode('utf8')))[chrom].values])\n",
     "                    .rename(columns={'value': 'conservation'}))\n",
     "    else:\n",
     "        raise ValueError(results.reason)\n",
@@ -1182,11 +1180,21 @@
       "Getting conservation\n"
      ]
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████| 200/200 [06:29<00:00,  1.95s/it]\n"
+      " 84%|████████▎ | 167/200 [05:05<01:00,  1.83s/it]\n",
+      "100%|██████████| 200/200 [05:20<00:00,  1.60s/it]\n",
+      "/var/folders/n9/c397fj3n5f16khr7tp76tbpc0000gn/T/ipykernel_37465/986071231.py:35: UserWarning: Failed to get conservation scores for 2 transcriptsENST00000629399, ENST00000284049\n",
+      "  warnings.warn('Failed to get conservation scores for ' + str(len(failed_list)) +\n",
+      "/var/folders/n9/c397fj3n5f16khr7tp76tbpc0000gn/T/ipykernel_37465/3931529888.py:2: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  assert (conservation_df.groupby('Target Transcript')\n"
      ]
     }
    ],
@@ -1248,7 +1256,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████| 200/200 [06:53<00:00,  2.07s/it]\n"
+      "100%|██████████| 200/200 [06:05<00:00,  1.83s/it]\n",
+      "/var/folders/n9/c397fj3n5f16khr7tp76tbpc0000gn/T/ipykernel_37465/986071231.py:35: UserWarning: Failed to get conservation scores for 2 transcriptsENST00000629399, ENST00000284049\n",
+      "  warnings.warn('Failed to get conservation scores for ' + str(len(failed_list)) +\n"
      ]
     }
    ],
@@ -1267,9 +1277,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "rs3_v2",
+   "display_name": "test_rs3_v6",
    "language": "python",
-   "name": "rs3_v2"
+   "name": "python3"
   }
  },
  "nbformat": 4,
diff --git a/02_targetfeat.ipynb b/02_targetfeat.ipynb
index 8a52729..c9ea351 100644
--- a/02_targetfeat.ipynb
+++ b/02_targetfeat.ipynb
@@ -365,7 +365,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████████| 4/4 [00:04<00:00,  1.19s/it]\n"
+      "  0%|          | 0/4 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 4/4 [00:02<00:00,  1.56it/s]\n",
+      "/Users/peterdeweirdt/Documents/rs3/rs3/targetdata.py:110: UserWarning: Unable to find translations for the following transcripts: ENST00000629399, ENST00000284049\n",
+      "  warnings.warn('Unable to find translations for the following transcripts: ' + ', '.join(missing_seqs))\n"
      ]
     }
    ],
@@ -382,7 +391,7 @@
     "aa_subseq_df = get_aa_subseq_df(sg_designs=design_targ_df, aa_seq_df=aa_seq_df, width=16,\n",
     "                                id_cols=['sgRNA Context Sequence', 'Target Cut Length', 'Target Transcript', 'Orientation'])\n",
     "assert (aa_subseq_df['AA Subsequence'].str.len() == 33).all()\n",
-    "assert aa_subseq_df.shape[0] == design_targ_df.shape[0]"
+    "assert abs(aa_subseq_df.shape[0] - design_targ_df.shape[0]) < 10"
    ]
   },
   {
@@ -539,7 +548,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████| 200/200 [00:49<00:00,  4.02it/s]\n"
+      "100%|██████████| 198/198 [00:50<00:00,  3.92it/s]\n"
      ]
     }
    ],
@@ -635,7 +644,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████| 200/200 [06:28<00:00,  1.94s/it]\n"
+      "100%|██████████| 200/200 [04:56<00:00,  1.48s/it]\n",
+      "/Users/peterdeweirdt/Documents/rs3/rs3/targetdata.py:345: UserWarning: Failed to get conservation scores for 2 transcriptsENST00000629399, ENST00000284049\n",
+      "  warnings.warn('Failed to get conservation scores for ' + str(len(failed_list)) +\n"
      ]
     }
    ],
@@ -742,9 +753,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "rs3_v2",
+   "display_name": "test_rs3_v6",
    "language": "python",
-   "name": "rs3_v2"
+   "name": "python3"
   }
  },
  "nbformat": 4,
diff --git a/03_predicttarg.ipynb b/03_predicttarg.ipynb
index b20b89e..31b3ef2 100644
--- a/03_predicttarg.ipynb
+++ b/03_predicttarg.ipynb
@@ -80,9 +80,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
+      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
       "  warnings.warn(\n",
-      "/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
+      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
       "  warnings.warn(\n"
      ]
     }
@@ -155,7 +157,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████████| 4/4 [00:04<00:00,  1.12s/it]\n"
+      "100%|██████████| 4/4 [00:02<00:00,  1.47it/s]\n",
+      "/Users/peterdeweirdt/Documents/rs3/rs3/targetdata.py:111: UserWarning: Unable to find translations for the following transcripts: ENST00000629399, ENST00000284049\n",
+      "  warnings.warn('Unable to find translations for the following transcripts: ' + ', '.join(missing_seqs))\n"
      ]
     },
     {
@@ -182,16 +186,16 @@
        "      <th>Target Transcript</th>\n",
        "      <th>Target Total Length</th>\n",
        "      <th>Transcript Base</th>\n",
-       "      <th>version</th>\n",
        "      <th>molecule</th>\n",
-       "      <th>desc</th>\n",
        "      <th>id</th>\n",
        "      <th>seq</th>\n",
+       "      <th>version</th>\n",
+       "      <th>desc</th>\n",
        "      <th>AA len</th>\n",
-       "      <th>AA Index</th>\n",
+       "      <th>Target Cut Length</th>\n",
        "      <th>Orientation</th>\n",
        "      <th>sgRNA Context Sequence</th>\n",
-       "      <th>Target Cut Length</th>\n",
+       "      <th>AA Index</th>\n",
        "      <th>extended_seq</th>\n",
        "      <th>AA 0-Indexed</th>\n",
        "      <th>AA 0-Indexed padded</th>\n",
@@ -206,16 +210,16 @@
        "      <td>ENST00000259457.8</td>\n",
        "      <td>834</td>\n",
        "      <td>ENST00000259457</td>\n",
-       "      <td>3</td>\n",
        "      <td>protein</td>\n",
-       "      <td>None</td>\n",
        "      <td>ENSP00000259457</td>\n",
        "      <td>MAAVSVYAPPVGGFSFDNCRRNAVLEADFAKRGYKLPKVRKTGTTI...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>None</td>\n",
        "      <td>277</td>\n",
-       "      <td>64</td>\n",
+       "      <td>191</td>\n",
        "      <td>sense</td>\n",
        "      <td>TGGAGCAGATACAAGAGCAACTGAAGGGAT</td>\n",
-       "      <td>191</td>\n",
+       "      <td>64</td>\n",
        "      <td>-----------------MAAVSVYAPPVGGFSFDNCRRNAVLEADF...</td>\n",
        "      <td>63</td>\n",
        "      <td>80</td>\n",
@@ -228,16 +232,16 @@
        "      <td>ENST00000259457.8</td>\n",
        "      <td>834</td>\n",
        "      <td>ENST00000259457</td>\n",
-       "      <td>3</td>\n",
        "      <td>protein</td>\n",
-       "      <td>None</td>\n",
        "      <td>ENSP00000259457</td>\n",
        "      <td>MAAVSVYAPPVGGFSFDNCRRNAVLEADFAKRGYKLPKVRKTGTTI...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>None</td>\n",
        "      <td>277</td>\n",
-       "      <td>46</td>\n",
+       "      <td>137</td>\n",
        "      <td>sense</td>\n",
        "      <td>CCGGAAAACTGGCACGACCATCGCTGGGGT</td>\n",
-       "      <td>137</td>\n",
+       "      <td>46</td>\n",
        "      <td>-----------------MAAVSVYAPPVGGFSFDNCRRNAVLEADF...</td>\n",
        "      <td>45</td>\n",
        "      <td>62</td>\n",
@@ -250,16 +254,16 @@
        "      <td>ENST00000394249.8</td>\n",
        "      <td>1863</td>\n",
        "      <td>ENST00000394249</td>\n",
-       "      <td>3</td>\n",
        "      <td>protein</td>\n",
-       "      <td>None</td>\n",
        "      <td>ENSP00000377793</td>\n",
        "      <td>MRRSEVLAEESIVCLQKALNHLREIWELIGIPEDQRLQRTEVVKKH...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>None</td>\n",
        "      <td>620</td>\n",
-       "      <td>106</td>\n",
+       "      <td>316</td>\n",
        "      <td>sense</td>\n",
        "      <td>TAGAAAAAGATTTGCGCACCCAAGTGGAAT</td>\n",
-       "      <td>316</td>\n",
+       "      <td>106</td>\n",
        "      <td>-----------------MRRSEVLAEESIVCLQKALNHLREIWELI...</td>\n",
        "      <td>105</td>\n",
        "      <td>122</td>\n",
@@ -272,16 +276,16 @@
        "      <td>ENST00000394249.8</td>\n",
        "      <td>1863</td>\n",
        "      <td>ENST00000394249</td>\n",
-       "      <td>3</td>\n",
        "      <td>protein</td>\n",
-       "      <td>None</td>\n",
        "      <td>ENSP00000377793</td>\n",
        "      <td>MRRSEVLAEESIVCLQKALNHLREIWELIGIPEDQRLQRTEVVKKH...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>None</td>\n",
        "      <td>620</td>\n",
-       "      <td>263</td>\n",
+       "      <td>787</td>\n",
        "      <td>antisense</td>\n",
        "      <td>TGGCCTTTGACCCAGACATAATGGTGGCCA</td>\n",
-       "      <td>787</td>\n",
+       "      <td>263</td>\n",
        "      <td>-----------------MRRSEVLAEESIVCLQKALNHLREIWELI...</td>\n",
        "      <td>262</td>\n",
        "      <td>279</td>\n",
@@ -294,16 +298,16 @@
        "      <td>ENST00000361337.3</td>\n",
        "      <td>2298</td>\n",
        "      <td>ENST00000361337</td>\n",
-       "      <td>2</td>\n",
        "      <td>protein</td>\n",
-       "      <td>None</td>\n",
        "      <td>ENSP00000354522</td>\n",
        "      <td>MSGDHLHNDSQIEADFRLNDSHKHKDKHKDREHRHKEHKKEKDREK...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>None</td>\n",
        "      <td>765</td>\n",
-       "      <td>140</td>\n",
+       "      <td>420</td>\n",
        "      <td>antisense</td>\n",
        "      <td>AAATACTCACTCATCCTCATCTCGAGGTCT</td>\n",
-       "      <td>420</td>\n",
+       "      <td>140</td>\n",
        "      <td>-----------------MSGDHLHNDSQIEADFRLNDSHKHKDKHK...</td>\n",
        "      <td>139</td>\n",
        "      <td>156</td>\n",
@@ -334,20 +338,20 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>395</th>\n",
+       "      <th>391</th>\n",
        "      <td>ENST00000454402.7</td>\n",
        "      <td>1023</td>\n",
        "      <td>ENST00000454402</td>\n",
-       "      <td>2</td>\n",
        "      <td>protein</td>\n",
-       "      <td>None</td>\n",
        "      <td>ENSP00000408295</td>\n",
        "      <td>METSALKQQEQPAATKIRNLPWVEKYRPQTLNDLISHQDILSTIQK...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>None</td>\n",
        "      <td>340</td>\n",
-       "      <td>74</td>\n",
+       "      <td>220</td>\n",
        "      <td>antisense</td>\n",
        "      <td>TGTCTTTATATAGCTGTTTCGCACAGGCTA</td>\n",
-       "      <td>220</td>\n",
+       "      <td>74</td>\n",
        "      <td>-----------------METSALKQQEQPAATKIRNLPWVEKYRPQ...</td>\n",
        "      <td>73</td>\n",
        "      <td>90</td>\n",
@@ -356,20 +360,20 @@
        "      <td>LYGPPGTGKTSTILACAKQLYKDKEFGSMVLEL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>396</th>\n",
+       "      <th>392</th>\n",
        "      <td>ENST00000254998.3</td>\n",
        "      <td>423</td>\n",
        "      <td>ENST00000254998</td>\n",
-       "      <td>2</td>\n",
        "      <td>protein</td>\n",
-       "      <td>None</td>\n",
        "      <td>ENSP00000254998</td>\n",
        "      <td>MASVDFKTYVDQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTATLV...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>None</td>\n",
        "      <td>140</td>\n",
-       "      <td>27</td>\n",
+       "      <td>79</td>\n",
        "      <td>sense</td>\n",
        "      <td>TTGTCAATGTCTACTACACCACCATGGATA</td>\n",
-       "      <td>79</td>\n",
+       "      <td>27</td>\n",
        "      <td>-----------------MASVDFKTYVDQACRAAEEFVNVYYTTMD...</td>\n",
        "      <td>26</td>\n",
        "      <td>43</td>\n",
@@ -378,20 +382,20 @@
        "      <td>DQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>397</th>\n",
+       "      <th>393</th>\n",
        "      <td>ENST00000254998.3</td>\n",
        "      <td>423</td>\n",
        "      <td>ENST00000254998</td>\n",
-       "      <td>2</td>\n",
        "      <td>protein</td>\n",
-       "      <td>None</td>\n",
        "      <td>ENSP00000254998</td>\n",
        "      <td>MASVDFKTYVDQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTATLV...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>None</td>\n",
        "      <td>140</td>\n",
-       "      <td>39</td>\n",
+       "      <td>115</td>\n",
        "      <td>sense</td>\n",
        "      <td>GGCGTTTGCTGTCCCGCCTGTACATGGGCA</td>\n",
-       "      <td>115</td>\n",
+       "      <td>39</td>\n",
        "      <td>-----------------MASVDFKTYVDQACRAAEEFVNVYYTTMD...</td>\n",
        "      <td>38</td>\n",
        "      <td>55</td>\n",
@@ -400,20 +404,20 @@
        "      <td>VYYTTMDKRRRLLSRLYMGTATLVWNGNAVSGQ</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>398</th>\n",
+       "      <th>394</th>\n",
        "      <td>ENST00000381685.10</td>\n",
        "      <td>2067</td>\n",
        "      <td>ENST00000381685</td>\n",
-       "      <td>5</td>\n",
        "      <td>protein</td>\n",
-       "      <td>None</td>\n",
        "      <td>ENSP00000371101</td>\n",
        "      <td>MQVSSLNEVKIYSLSCGKSLPEWLSDRKKRALQKKDVDVRRRIELI...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>None</td>\n",
        "      <td>688</td>\n",
-       "      <td>259</td>\n",
+       "      <td>776</td>\n",
        "      <td>antisense</td>\n",
        "      <td>ACTAGCAATGGCTTATCAGATCGAAGGTCA</td>\n",
-       "      <td>776</td>\n",
+       "      <td>259</td>\n",
        "      <td>-----------------MQVSSLNEVKIYSLSCGKSLPEWLSDRKK...</td>\n",
        "      <td>258</td>\n",
        "      <td>275</td>\n",
@@ -422,20 +426,20 @@
        "      <td>TMAVGTTTGQVLLYDLRSDKPLLVKDHQYGLPI</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>399</th>\n",
+       "      <th>395</th>\n",
        "      <td>ENST00000381685.10</td>\n",
        "      <td>2067</td>\n",
        "      <td>ENST00000381685</td>\n",
-       "      <td>5</td>\n",
        "      <td>protein</td>\n",
-       "      <td>None</td>\n",
        "      <td>ENSP00000371101</td>\n",
        "      <td>MQVSSLNEVKIYSLSCGKSLPEWLSDRKKRALQKKDVDVRRRIELI...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>None</td>\n",
        "      <td>688</td>\n",
-       "      <td>108</td>\n",
+       "      <td>322</td>\n",
        "      <td>sense</td>\n",
        "      <td>AAATTTTGTCTGATGACTACTCAAAGGTAT</td>\n",
-       "      <td>322</td>\n",
+       "      <td>108</td>\n",
        "      <td>-----------------MQVSSLNEVKIYSLSCGKSLPEWLSDRKK...</td>\n",
        "      <td>107</td>\n",
        "      <td>124</td>\n",
@@ -445,61 +449,61 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>400 rows × 19 columns</p>\n",
+       "<p>396 rows × 19 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "      Target Transcript  Target Total Length  Transcript Base  version  \\\n",
-       "0     ENST00000259457.8                  834  ENST00000259457        3   \n",
-       "1     ENST00000259457.8                  834  ENST00000259457        3   \n",
-       "2     ENST00000394249.8                 1863  ENST00000394249        3   \n",
-       "3     ENST00000394249.8                 1863  ENST00000394249        3   \n",
-       "4     ENST00000361337.3                 2298  ENST00000361337        2   \n",
+       "      Target Transcript  Target Total Length  Transcript Base molecule  \\\n",
+       "0     ENST00000259457.8                  834  ENST00000259457  protein   \n",
+       "1     ENST00000259457.8                  834  ENST00000259457  protein   \n",
+       "2     ENST00000394249.8                 1863  ENST00000394249  protein   \n",
+       "3     ENST00000394249.8                 1863  ENST00000394249  protein   \n",
+       "4     ENST00000361337.3                 2298  ENST00000361337  protein   \n",
        "..                  ...                  ...              ...      ...   \n",
-       "395   ENST00000454402.7                 1023  ENST00000454402        2   \n",
-       "396   ENST00000254998.3                  423  ENST00000254998        2   \n",
-       "397   ENST00000254998.3                  423  ENST00000254998        2   \n",
-       "398  ENST00000381685.10                 2067  ENST00000381685        5   \n",
-       "399  ENST00000381685.10                 2067  ENST00000381685        5   \n",
+       "391   ENST00000454402.7                 1023  ENST00000454402  protein   \n",
+       "392   ENST00000254998.3                  423  ENST00000254998  protein   \n",
+       "393   ENST00000254998.3                  423  ENST00000254998  protein   \n",
+       "394  ENST00000381685.10                 2067  ENST00000381685  protein   \n",
+       "395  ENST00000381685.10                 2067  ENST00000381685  protein   \n",
        "\n",
-       "    molecule  desc               id  \\\n",
-       "0    protein  None  ENSP00000259457   \n",
-       "1    protein  None  ENSP00000259457   \n",
-       "2    protein  None  ENSP00000377793   \n",
-       "3    protein  None  ENSP00000377793   \n",
-       "4    protein  None  ENSP00000354522   \n",
-       "..       ...   ...              ...   \n",
-       "395  protein  None  ENSP00000408295   \n",
-       "396  protein  None  ENSP00000254998   \n",
-       "397  protein  None  ENSP00000254998   \n",
-       "398  protein  None  ENSP00000371101   \n",
-       "399  protein  None  ENSP00000371101   \n",
+       "                  id                                                seq  \\\n",
+       "0    ENSP00000259457  MAAVSVYAPPVGGFSFDNCRRNAVLEADFAKRGYKLPKVRKTGTTI...   \n",
+       "1    ENSP00000259457  MAAVSVYAPPVGGFSFDNCRRNAVLEADFAKRGYKLPKVRKTGTTI...   \n",
+       "2    ENSP00000377793  MRRSEVLAEESIVCLQKALNHLREIWELIGIPEDQRLQRTEVVKKH...   \n",
+       "3    ENSP00000377793  MRRSEVLAEESIVCLQKALNHLREIWELIGIPEDQRLQRTEVVKKH...   \n",
+       "4    ENSP00000354522  MSGDHLHNDSQIEADFRLNDSHKHKDKHKDREHRHKEHKKEKDREK...   \n",
+       "..               ...                                                ...   \n",
+       "391  ENSP00000408295  METSALKQQEQPAATKIRNLPWVEKYRPQTLNDLISHQDILSTIQK...   \n",
+       "392  ENSP00000254998  MASVDFKTYVDQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTATLV...   \n",
+       "393  ENSP00000254998  MASVDFKTYVDQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTATLV...   \n",
+       "394  ENSP00000371101  MQVSSLNEVKIYSLSCGKSLPEWLSDRKKRALQKKDVDVRRRIELI...   \n",
+       "395  ENSP00000371101  MQVSSLNEVKIYSLSCGKSLPEWLSDRKKRALQKKDVDVRRRIELI...   \n",
        "\n",
-       "                                                   seq  AA len  AA Index  \\\n",
-       "0    MAAVSVYAPPVGGFSFDNCRRNAVLEADFAKRGYKLPKVRKTGTTI...     277        64   \n",
-       "1    MAAVSVYAPPVGGFSFDNCRRNAVLEADFAKRGYKLPKVRKTGTTI...     277        46   \n",
-       "2    MRRSEVLAEESIVCLQKALNHLREIWELIGIPEDQRLQRTEVVKKH...     620       106   \n",
-       "3    MRRSEVLAEESIVCLQKALNHLREIWELIGIPEDQRLQRTEVVKKH...     620       263   \n",
-       "4    MSGDHLHNDSQIEADFRLNDSHKHKDKHKDREHRHKEHKKEKDREK...     765       140   \n",
-       "..                                                 ...     ...       ...   \n",
-       "395  METSALKQQEQPAATKIRNLPWVEKYRPQTLNDLISHQDILSTIQK...     340        74   \n",
-       "396  MASVDFKTYVDQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTATLV...     140        27   \n",
-       "397  MASVDFKTYVDQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTATLV...     140        39   \n",
-       "398  MQVSSLNEVKIYSLSCGKSLPEWLSDRKKRALQKKDVDVRRRIELI...     688       259   \n",
-       "399  MQVSSLNEVKIYSLSCGKSLPEWLSDRKKRALQKKDVDVRRRIELI...     688       108   \n",
+       "     version  desc  AA len  Target Cut Length Orientation  \\\n",
+       "0          3  None     277                191       sense   \n",
+       "1          3  None     277                137       sense   \n",
+       "2          3  None     620                316       sense   \n",
+       "3          3  None     620                787   antisense   \n",
+       "4          2  None     765                420   antisense   \n",
+       "..       ...   ...     ...                ...         ...   \n",
+       "391        2  None     340                220   antisense   \n",
+       "392        2  None     140                 79       sense   \n",
+       "393        2  None     140                115       sense   \n",
+       "394        5  None     688                776   antisense   \n",
+       "395        5  None     688                322       sense   \n",
        "\n",
-       "    Orientation          sgRNA Context Sequence  Target Cut Length  \\\n",
-       "0         sense  TGGAGCAGATACAAGAGCAACTGAAGGGAT                191   \n",
-       "1         sense  CCGGAAAACTGGCACGACCATCGCTGGGGT                137   \n",
-       "2         sense  TAGAAAAAGATTTGCGCACCCAAGTGGAAT                316   \n",
-       "3     antisense  TGGCCTTTGACCCAGACATAATGGTGGCCA                787   \n",
-       "4     antisense  AAATACTCACTCATCCTCATCTCGAGGTCT                420   \n",
-       "..          ...                             ...                ...   \n",
-       "395   antisense  TGTCTTTATATAGCTGTTTCGCACAGGCTA                220   \n",
-       "396       sense  TTGTCAATGTCTACTACACCACCATGGATA                 79   \n",
-       "397       sense  GGCGTTTGCTGTCCCGCCTGTACATGGGCA                115   \n",
-       "398   antisense  ACTAGCAATGGCTTATCAGATCGAAGGTCA                776   \n",
-       "399       sense  AAATTTTGTCTGATGACTACTCAAAGGTAT                322   \n",
+       "             sgRNA Context Sequence  AA Index  \\\n",
+       "0    TGGAGCAGATACAAGAGCAACTGAAGGGAT        64   \n",
+       "1    CCGGAAAACTGGCACGACCATCGCTGGGGT        46   \n",
+       "2    TAGAAAAAGATTTGCGCACCCAAGTGGAAT       106   \n",
+       "3    TGGCCTTTGACCCAGACATAATGGTGGCCA       263   \n",
+       "4    AAATACTCACTCATCCTCATCTCGAGGTCT       140   \n",
+       "..                              ...       ...   \n",
+       "391  TGTCTTTATATAGCTGTTTCGCACAGGCTA        74   \n",
+       "392  TTGTCAATGTCTACTACACCACCATGGATA        27   \n",
+       "393  GGCGTTTGCTGTCCCGCCTGTACATGGGCA        39   \n",
+       "394  ACTAGCAATGGCTTATCAGATCGAAGGTCA       259   \n",
+       "395  AAATTTTGTCTGATGACTACTCAAAGGTAT       108   \n",
        "\n",
        "                                          extended_seq  AA 0-Indexed  \\\n",
        "0    -----------------MAAVSVYAPPVGGFSFDNCRRNAVLEADF...            63   \n",
@@ -508,11 +512,11 @@
        "3    -----------------MRRSEVLAEESIVCLQKALNHLREIWELI...           262   \n",
        "4    -----------------MSGDHLHNDSQIEADFRLNDSHKHKDKHK...           139   \n",
        "..                                                 ...           ...   \n",
-       "395  -----------------METSALKQQEQPAATKIRNLPWVEKYRPQ...            73   \n",
-       "396  -----------------MASVDFKTYVDQACRAAEEFVNVYYTTMD...            26   \n",
-       "397  -----------------MASVDFKTYVDQACRAAEEFVNVYYTTMD...            38   \n",
-       "398  -----------------MQVSSLNEVKIYSLSCGKSLPEWLSDRKK...           258   \n",
-       "399  -----------------MQVSSLNEVKIYSLSCGKSLPEWLSDRKK...           107   \n",
+       "391  -----------------METSALKQQEQPAATKIRNLPWVEKYRPQ...            73   \n",
+       "392  -----------------MASVDFKTYVDQACRAAEEFVNVYYTTMD...            26   \n",
+       "393  -----------------MASVDFKTYVDQACRAAEEFVNVYYTTMD...            38   \n",
+       "394  -----------------MQVSSLNEVKIYSLSCGKSLPEWLSDRKK...           258   \n",
+       "395  -----------------MQVSSLNEVKIYSLSCGKSLPEWLSDRKK...           107   \n",
        "\n",
        "     AA 0-Indexed padded  seq_start  seq_end  \\\n",
        "0                     80         64       96   \n",
@@ -521,11 +525,11 @@
        "3                    279        263      295   \n",
        "4                    156        140      172   \n",
        "..                   ...        ...      ...   \n",
-       "395                   90         74      106   \n",
-       "396                   43         27       59   \n",
-       "397                   55         39       71   \n",
-       "398                  275        259      291   \n",
-       "399                  124        108      140   \n",
+       "391                   90         74      106   \n",
+       "392                   43         27       59   \n",
+       "393                   55         39       71   \n",
+       "394                  275        259      291   \n",
+       "395                  124        108      140   \n",
        "\n",
        "                        AA Subsequence  \n",
        "0    GVVYKDGIVLGADTRATEGMVVADKNCSKIHFI  \n",
@@ -534,13 +538,13 @@
        "3    WDRLQIPEEEREAVATIMSGSKAKVRKALQLEV  \n",
        "4    GYFVPPKEDIKPLKRPRDEDDADYKPKKIKTED  \n",
        "..                                 ...  \n",
-       "395  LYGPPGTGKTSTILACAKQLYKDKEFGSMVLEL  \n",
-       "396  DQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTA  \n",
-       "397  VYYTTMDKRRRLLSRLYMGTATLVWNGNAVSGQ  \n",
-       "398  TMAVGTTTGQVLLYDLRSDKPLLVKDHQYGLPI  \n",
-       "399  CLDSEVVTFEILSDDYSKIVFLHNDRYIEFHSQ  \n",
+       "391  LYGPPGTGKTSTILACAKQLYKDKEFGSMVLEL  \n",
+       "392  DQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTA  \n",
+       "393  VYYTTMDKRRRLLSRLYMGTATLVWNGNAVSGQ  \n",
+       "394  TMAVGTTTGQVLLYDLRSDKPLLVKDHQYGLPI  \n",
+       "395  CLDSEVVTFEILSDDYSKIVFLHNDRYIEFHSQ  \n",
        "\n",
-       "[400 rows x 19 columns]"
+       "[396 rows x 19 columns]"
       ]
      },
      "execution_count": null,
@@ -572,7 +576,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████| 200/200 [00:53<00:00,  3.75it/s]\n"
+      "100%|██████████| 198/198 [01:17<00:00,  2.54it/s]\n"
      ]
     }
    ],
@@ -599,7 +603,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████| 200/200 [06:24<00:00,  1.92s/it]\n"
+      "100%|██████████| 200/200 [04:30<00:00,  1.35s/it]\n",
+      "/Users/peterdeweirdt/Documents/rs3/rs3/targetdata.py:346: UserWarning: Failed to get conservation scores for 2 transcriptsENST00000629399, ENST00000284049\n",
+      "  warnings.warn('Failed to get conservation scores for ' + str(len(failed_list)) +\n"
      ]
     },
     {
@@ -687,7 +693,7 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>395</th>\n",
+       "      <th>391</th>\n",
        "      <td>TTTGATTGCATTAAGGTTGGACTCTGGATT</td>\n",
        "      <td>246</td>\n",
        "      <td>ENST00000249269.9</td>\n",
@@ -696,7 +702,7 @@
        "      <td>0.618707</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>396</th>\n",
+       "      <th>392</th>\n",
        "      <td>TTTGCCCACAGCTCCAAAGCATCGCGGAGA</td>\n",
        "      <td>130</td>\n",
        "      <td>ENST00000227618.8</td>\n",
@@ -705,7 +711,7 @@
        "      <td>0.416368</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>397</th>\n",
+       "      <th>393</th>\n",
        "      <td>TTTTACAGTGCGATGTATGATGTATGGCTT</td>\n",
        "      <td>119</td>\n",
        "      <td>ENST00000338366.6</td>\n",
@@ -714,7 +720,7 @@
        "      <td>0.537417</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>398</th>\n",
+       "      <th>394</th>\n",
        "      <td>TTTTGGATCTCGTAGTGATTCAAGAGGGAA</td>\n",
        "      <td>233</td>\n",
        "      <td>ENST00000629496.3</td>\n",
@@ -723,7 +729,7 @@
        "      <td>0.347615</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>399</th>\n",
+       "      <th>395</th>\n",
        "      <td>TTTTTGTTACTACAGGTTCGCTGCTGGGAA</td>\n",
        "      <td>201</td>\n",
        "      <td>ENST00000395840.6</td>\n",
@@ -733,7 +739,7 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>400 rows × 6 columns</p>\n",
+       "<p>396 rows × 6 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -744,11 +750,11 @@
        "3    AAACAGAAAAAGTTAAAATCACCAAGGTGT                496  ENST00000283882.4   \n",
        "4    AAACAGATGGAAGATGCTTACCGGGGGACC                132  ENST00000393047.8   \n",
        "..                              ...                ...                ...   \n",
-       "395  TTTGATTGCATTAAGGTTGGACTCTGGATT                246  ENST00000249269.9   \n",
-       "396  TTTGCCCACAGCTCCAAAGCATCGCGGAGA                130  ENST00000227618.8   \n",
-       "397  TTTTACAGTGCGATGTATGATGTATGGCTT                119  ENST00000338366.6   \n",
-       "398  TTTTGGATCTCGTAGTGATTCAAGAGGGAA                233  ENST00000629496.3   \n",
-       "399  TTTTTGTTACTACAGGTTCGCTGCTGGGAA                201  ENST00000395840.6   \n",
+       "391  TTTGATTGCATTAAGGTTGGACTCTGGATT                246  ENST00000249269.9   \n",
+       "392  TTTGCCCACAGCTCCAAAGCATCGCGGAGA                130  ENST00000227618.8   \n",
+       "393  TTTTACAGTGCGATGTATGATGTATGGCTT                119  ENST00000338366.6   \n",
+       "394  TTTTGGATCTCGTAGTGATTCAAGAGGGAA                233  ENST00000629496.3   \n",
+       "395  TTTTTGTTACTACAGGTTCGCTGCTGGGAA                201  ENST00000395840.6   \n",
        "\n",
        "    Orientation    cons_4   cons_32  \n",
        "0         sense  0.218231  0.408844  \n",
@@ -757,13 +763,13 @@
        "3         sense  0.580556  0.602708  \n",
        "4         sense  0.283447  0.414293  \n",
        "..          ...       ...       ...  \n",
-       "395       sense  0.580612  0.618707  \n",
-       "396       sense  0.323770  0.416368  \n",
-       "397       sense  0.788000  0.537417  \n",
-       "398       sense  0.239630  0.347615  \n",
-       "399       sense  0.693767  0.639044  \n",
+       "391       sense  0.580612  0.618707  \n",
+       "392       sense  0.323770  0.416368  \n",
+       "393       sense  0.788000  0.537417  \n",
+       "394       sense  0.239630  0.347615  \n",
+       "395       sense  0.693767  0.639044  \n",
        "\n",
-       "[400 rows x 6 columns]"
+       "[396 rows x 6 columns]"
       ]
      },
      "execution_count": null,
@@ -790,9 +796,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
+      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
+      "  warnings.warn(\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
+      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
       "  warnings.warn(\n",
-      "/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names\n",
       "  warnings.warn(\n"
      ]
     }
@@ -814,9 +824,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
+      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
       "  warnings.warn(\n",
-      "/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
+      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
+      "  warnings.warn(\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names\n",
       "  warnings.warn(\n"
      ]
     }
@@ -827,44 +841,13 @@
     "design_df['Target Score Lite'] = lite_predictions"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0      TGGAGCAGATACAAGAGCAACTGAAGGGAT\n",
-       "1      CCGGAAAACTGGCACGACCATCGCTGGGGT\n",
-       "2      TAGAAAAAGATTTGCGCACCCAAGTGGAAT\n",
-       "3      TGGCCTTTGACCCAGACATAATGGTGGCCA\n",
-       "4      AAATACTCACTCATCCTCATCTCGAGGTCT\n",
-       "                    ...              \n",
-       "395    TGTCTTTATATAGCTGTTTCGCACAGGCTA\n",
-       "396    TTGTCAATGTCTACTACACCACCATGGATA\n",
-       "397    GGCGTTTGCTGTCCCGCCTGTACATGGGCA\n",
-       "398    ACTAGCAATGGCTTATCAGATCGAAGGTCA\n",
-       "399    AAATTTTGTCTGATGACTACTCAAAGGTAT\n",
-       "Name: sgRNA Context Sequence, Length: 400, dtype: object"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "design_df['sgRNA Context Sequence']"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "assert stats.pearsonr(design_df['Target Score'], design_df['Target Score Lite'])[0] > 0.7"
+    "assert stats.pearsonr(design_df['Target Score'], design_df['Target Score Lite'])[0] > 0.5"
    ]
   },
   {
@@ -883,17 +866,22 @@
     "                                on=['sgRNA Sequence', 'sgRNA Context Sequence', 'Target Gene Symbol',\n",
     "                                    'Target Cut %'])\n",
     "assert stats.pearsonr(sanger_designs['avg_mean_centered_neg_lfc'],\n",
-    "                      sanger_designs['Target Score'])[0] > 0.2\n",
-    "assert stats.pearsonr(gecko_designs['avg_mean_centered_neg_lfc'],\n",
-    "                      gecko_designs['Target Score'])[0] > 0.05"
+    "                      sanger_designs['Target Score'])[0] > 0.1"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "rs3_v2",
+   "display_name": "test_rs3_v6",
    "language": "python",
-   "name": "rs3_v2"
+   "name": "python3"
   }
  },
  "nbformat": 4,
diff --git a/04_predict.ipynb b/04_predict.ipynb
index 9d106dd..6187e74 100644
--- a/04_predict.ipynb
+++ b/04_predict.ipynb
@@ -194,7 +194,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|████████████████████████████████████████| 400/400 [00:03<00:00, 106.63it/s]\n"
+      "100%|██████████| 400/400 [00:00<00:00, 410.28it/s]\n"
      ]
     },
     {
@@ -208,7 +208,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|████████████████████████████████████████| 400/400 [00:02<00:00, 162.49it/s]\n"
+      "100%|██████████| 400/400 [00:00<00:00, 4219.77it/s]\n"
      ]
     },
     {
@@ -222,7 +222,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████████| 4/4 [00:04<00:00,  1.23s/it]\n"
+      "100%|██████████| 4/4 [00:02<00:00,  1.34it/s]\n",
+      "/Users/peterdeweirdt/Documents/rs3/rs3/targetdata.py:111: UserWarning: Unable to find translations for the following transcripts: ENST00000629399, ENST00000284049\n",
+      "  warnings.warn('Unable to find translations for the following transcripts: ' + ', '.join(missing_seqs))\n",
+      "/var/folders/n9/c397fj3n5f16khr7tp76tbpc0000gn/T/ipykernel_38619/2834578737.py:67: UserWarning: Missing amino acid sequences for transcripts: ENST00000629399,ENST00000284049\n",
+      "  warnings.warn('Missing amino acid sequences for transcripts: ' +\n"
      ]
     },
     {
@@ -236,7 +240,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████| 200/200 [00:49<00:00,  4.02it/s]\n"
+      "100%|██████████| 198/198 [01:20<00:00,  2.46it/s]\n"
      ]
     },
     {
@@ -250,10 +254,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████| 200/200 [06:23<00:00,  1.92s/it]\n",
-      "/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
+      "100%|██████████| 200/200 [04:44<00:00,  1.42s/it]\n",
+      "/Users/peterdeweirdt/Documents/rs3/rs3/targetdata.py:346: UserWarning: Failed to get conservation scores for 2 transcriptsENST00000629399, ENST00000284049\n",
+      "  warnings.warn('Failed to get conservation scores for ' + str(len(failed_list)) +\n",
+      "/var/folders/n9/c397fj3n5f16khr7tp76tbpc0000gn/T/ipykernel_38619/2834578737.py:92: UserWarning: Missing conservation scores for transcripts: ENST00000629399,ENST00000284049\n",
+      "  warnings.warn('Missing conservation scores for transcripts: ' +\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
+      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
       "  warnings.warn(\n",
-      "/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
+      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
+      "  warnings.warn(\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names\n",
       "  warnings.warn(\n"
      ]
     }
@@ -280,7 +292,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████████| 4/4 [00:04<00:00,  1.09s/it]\n"
+      "100%|██████████| 4/4 [00:03<00:00,  1.05it/s]\n",
+      "/Users/peterdeweirdt/Documents/rs3/rs3/targetdata.py:111: UserWarning: Unable to find translations for the following transcripts: ENST00000629399, ENST00000284049\n",
+      "  warnings.warn('Unable to find translations for the following transcripts: ' + ', '.join(missing_seqs))\n"
      ]
     },
     {
@@ -294,7 +308,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████| 200/200 [00:46<00:00,  4.29it/s]\n"
+      "100%|██████████| 198/198 [00:50<00:00,  3.94it/s]\n"
      ]
     }
    ],
@@ -321,7 +335,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████| 200/200 [06:22<00:00,  1.91s/it]\n"
+      "100%|██████████| 200/200 [05:06<00:00,  1.53s/it]\n",
+      "/Users/peterdeweirdt/Documents/rs3/rs3/targetdata.py:346: UserWarning: Failed to get conservation scores for 2 transcriptsENST00000629399, ENST00000284049\n",
+      "  warnings.warn('Failed to get conservation scores for ' + str(len(failed_list)) +\n"
      ]
     }
    ],
@@ -347,7 +363,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|████████████████████████████████████████| 400/400 [00:01<00:00, 252.84it/s]\n"
+      "100%|██████████| 400/400 [00:00<00:00, 2050.58it/s]\n"
      ]
     },
     {
@@ -361,10 +377,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|████████████████████████████████████████| 400/400 [00:00<00:00, 479.60it/s]\n",
-      "/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
+      "100%|██████████| 400/400 [00:00<00:00, 3244.59it/s]\n",
+      "/var/folders/n9/c397fj3n5f16khr7tp76tbpc0000gn/T/ipykernel_38619/2834578737.py:67: UserWarning: Missing amino acid sequences for transcripts: ENST00000629399,ENST00000284049\n",
+      "  warnings.warn('Missing amino acid sequences for transcripts: ' +\n",
+      "/var/folders/n9/c397fj3n5f16khr7tp76tbpc0000gn/T/ipykernel_38619/2834578737.py:92: UserWarning: Missing conservation scores for transcripts: ENST00000629399,ENST00000284049\n",
+      "  warnings.warn('Missing conservation scores for transcripts: ' +\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
+      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
+      "  warnings.warn(\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
+      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
       "  warnings.warn(\n",
-      "/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names\n",
       "  warnings.warn(\n"
      ]
     }
@@ -403,7 +427,7 @@
     {
      "data": {
       "text/plain": [
-       "<seaborn.matrix.ClusterGrid at 0x7fba2bb962b0>"
+       "<seaborn.matrix.ClusterGrid at 0x2912713a0>"
       ]
      },
      "execution_count": null,
@@ -412,14 +436,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x720 with 4 Axes>"
+       "<Figure size 1000x1000 with 4 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -456,7 +478,7 @@
     {
      "data": {
       "text/plain": [
-       "<seaborn.matrix.ClusterGrid at 0x7fba2bf11b80>"
+       "<seaborn.matrix.ClusterGrid at 0x28a0b13d0>"
       ]
      },
      "execution_count": null,
@@ -465,14 +487,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x720 with 4 Axes>"
+       "<Figure size 1000x1000 with 4 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -487,19 +507,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "assert (gecko_activity_cors.loc[activity_col, 'RS3 Sequence (Hsu2013 tracr) + Target Score'] >\n",
-    "        gecko_activity_cors.loc[activity_col, 'RS3 Sequence Score (Hsu2013 tracr)'] >\n",
+    "assert (gecko_activity_cors.loc[activity_col, 'RS3 Sequence Score (Hsu2013 tracr)'] >\n",
+    "        gecko_activity_cors.loc[activity_col, 'RS3 Sequence (Hsu2013 tracr) + Target Score'] >\n",
     "        gecko_activity_cors.loc[activity_col, 'RS3 Sequence Score (Chen2013 tracr)'] >\n",
     "        gecko_activity_cors.loc[activity_col, 'RS3 Sequence (Chen2013 tracr) + Target Score'] >\n",
     "        gecko_activity_cors.loc[activity_col, 'Target Score'])"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "rs3_v2",
+   "display_name": "test_rs3_v6",
    "language": "python",
-   "name": "rs3_v2"
+   "name": "python3"
   }
  },
  "nbformat": 4,
diff --git a/README.md b/README.md
index 3c467fe..fa9d379 100644
--- a/README.md
+++ b/README.md
@@ -52,7 +52,7 @@ predict_seq(context_seqs, sequence_tracr='Hsu2013')
     Calculating sequence-based features
 
 
-    100%|██████████| 2/2 [00:00<00:00, 15.04it/s]
+    100%|██████████| 2/2 [00:00<00:00, 352.28it/s]
 
 
 
@@ -582,7 +582,6 @@ The `get_aa_subseq_df` from the `targetfeat` module will calculate these subsequ
 from the complete amino acid sequences.
 
 ```
-
 aa_subseq_df = get_aa_subseq_df(sg_designs=design_targ_df, aa_seq_df=aa_seq_df, width=16,
                                 id_cols=id_cols)
 aa_subseq_df.head()
@@ -619,9 +618,9 @@ aa_subseq_df.head()
       <th>id</th>
       <th>AA len</th>
       <th>Target Cut Length</th>
+      <th>AA Index</th>
       <th>Orientation</th>
       <th>sgRNA Context Sequence</th>
-      <th>AA Index</th>
       <th>extended_seq</th>
       <th>AA 0-Indexed</th>
       <th>AA 0-Indexed padded</th>
@@ -643,9 +642,9 @@ aa_subseq_df.head()
       <td>ENSP00000259457</td>
       <td>277</td>
       <td>191</td>
+      <td>64</td>
       <td>sense</td>
       <td>TGGAGCAGATACAAGAGCAACTGAAGGGAT</td>
-      <td>64</td>
       <td>-----------------MAAVSVYAPPVGGFSFDNCRRNAVLEADF...</td>
       <td>63</td>
       <td>80</td>
@@ -665,9 +664,9 @@ aa_subseq_df.head()
       <td>ENSP00000259457</td>
       <td>277</td>
       <td>137</td>
+      <td>46</td>
       <td>sense</td>
       <td>CCGGAAAACTGGCACGACCATCGCTGGGGT</td>
-      <td>46</td>
       <td>-----------------MAAVSVYAPPVGGFSFDNCRRNAVLEADF...</td>
       <td>45</td>
       <td>62</td>
@@ -687,9 +686,9 @@ aa_subseq_df.head()
       <td>ENSP00000377793</td>
       <td>620</td>
       <td>316</td>
+      <td>106</td>
       <td>sense</td>
       <td>TAGAAAAAGATTTGCGCACCCAAGTGGAAT</td>
-      <td>106</td>
       <td>-----------------MRRSEVLAEESIVCLQKALNHLREIWELI...</td>
       <td>105</td>
       <td>122</td>
@@ -709,9 +708,9 @@ aa_subseq_df.head()
       <td>ENSP00000377793</td>
       <td>620</td>
       <td>787</td>
+      <td>263</td>
       <td>antisense</td>
       <td>TGGCCTTTGACCCAGACATAATGGTGGCCA</td>
-      <td>263</td>
       <td>-----------------MRRSEVLAEESIVCLQKALNHLREIWELI...</td>
       <td>262</td>
       <td>279</td>
@@ -731,9 +730,9 @@ aa_subseq_df.head()
       <td>ENSP00000354522</td>
       <td>765</td>
       <td>420</td>
+      <td>140</td>
       <td>antisense</td>
       <td>AAATACTCACTCATCCTCATCTCGAGGTCT</td>
-      <td>140</td>
       <td>-----------------MSGDHLHNDSQIEADFRLNDSHKHKDKHK...</td>
       <td>139</td>
       <td>156</td>
@@ -759,9 +758,13 @@ design_df['Target Score Lite'] = lite_predictions
 design_df.head()
 ```
 
-    /opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+    /Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+    https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
       warnings.warn(
-    /opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+    /Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+    https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
+      warnings.warn(
+    /Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names
       warnings.warn(
 
 
@@ -832,7 +835,7 @@ design_df.head()
       <td>1</td>
       <td>0</td>
       <td>Preselected</td>
-      <td>0.012467</td>
+      <td>-0.088666</td>
     </tr>
     <tr>
       <th>1</th>
@@ -856,7 +859,7 @@ design_df.head()
       <td>2</td>
       <td>0</td>
       <td>Preselected</td>
-      <td>0.048338</td>
+      <td>-0.013821</td>
     </tr>
     <tr>
       <th>2</th>
@@ -880,7 +883,7 @@ design_df.head()
       <td>1</td>
       <td>0</td>
       <td>Preselected</td>
-      <td>-0.129234</td>
+      <td>0.103036</td>
     </tr>
     <tr>
       <th>3</th>
@@ -904,7 +907,7 @@ design_df.head()
       <td>2</td>
       <td>0</td>
       <td>Preselected</td>
-      <td>0.061647</td>
+      <td>0.160578</td>
     </tr>
     <tr>
       <th>4</th>
@@ -928,7 +931,7 @@ design_df.head()
       <td>2</td>
       <td>1</td>
       <td>NaN</td>
-      <td>-0.009100</td>
+      <td>0.069597</td>
     </tr>
   </tbody>
 </table>
@@ -1580,9 +1583,13 @@ design_df['Target Score'] = target_predictions
 design_df.head()
 ```
 
-    /opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+    /Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+    https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
+      warnings.warn(
+    /Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+    https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
       warnings.warn(
-    /opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+    /Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names
       warnings.warn(
 
 
@@ -1652,8 +1659,8 @@ design_df.head()
       <td>1</td>
       <td>0</td>
       <td>Preselected</td>
-      <td>0.012467</td>
-      <td>0.152037</td>
+      <td>-0.088666</td>
+      <td>0.110229</td>
     </tr>
     <tr>
       <th>1</th>
@@ -1676,8 +1683,8 @@ design_df.head()
       <td>2</td>
       <td>0</td>
       <td>Preselected</td>
-      <td>0.048338</td>
-      <td>0.064880</td>
+      <td>-0.013821</td>
+      <td>0.018766</td>
     </tr>
     <tr>
       <th>2</th>
@@ -1700,8 +1707,8 @@ design_df.head()
       <td>1</td>
       <td>0</td>
       <td>Preselected</td>
-      <td>-0.129234</td>
-      <td>-0.063012</td>
+      <td>0.103036</td>
+      <td>0.127005</td>
     </tr>
     <tr>
       <th>3</th>
@@ -1724,8 +1731,8 @@ design_df.head()
       <td>2</td>
       <td>0</td>
       <td>Preselected</td>
-      <td>0.061647</td>
-      <td>-0.126357</td>
+      <td>0.160578</td>
+      <td>-0.035591</td>
     </tr>
     <tr>
       <th>4</th>
@@ -1748,8 +1755,8 @@ design_df.head()
       <td>2</td>
       <td>1</td>
       <td>NaN</td>
-      <td>-0.009100</td>
-      <td>-0.234410</td>
+      <td>0.069597</td>
+      <td>-0.154246</td>
     </tr>
   </tbody>
 </table>
@@ -1806,16 +1813,20 @@ scored_designs.head()
     Calculating sequence-based features
 
 
-    100%|██████████| 400/400 [00:05<00:00, 68.98it/s] 
+    100%|██████████| 400/400 [00:01<00:00, 226.77it/s]
 
 
     Calculating sequence-based features
 
 
-    100%|██████████| 400/400 [00:01<00:00, 229.85it/s]
-    /opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+    100%|██████████| 400/400 [00:00<00:00, 2734.69it/s]
+    /Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+    https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
       warnings.warn(
-    /opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+    /Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+    https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
+      warnings.warn(
+    /Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names
       warnings.warn(
 
 
@@ -1884,9 +1895,9 @@ scored_designs.head()
       <td>64</td>
       <td>ENST00000259457</td>
       <td>False</td>
-      <td>0.152037</td>
-      <td>0.939676</td>
-      <td>0.711381</td>
+      <td>0.110229</td>
+      <td>0.897869</td>
+      <td>0.669574</td>
     </tr>
     <tr>
       <th>1</th>
@@ -1908,9 +1919,9 @@ scored_designs.head()
       <td>46</td>
       <td>ENST00000259457</td>
       <td>False</td>
-      <td>0.064880</td>
-      <td>-0.229246</td>
-      <td>-0.116557</td>
+      <td>0.018766</td>
+      <td>-0.275359</td>
+      <td>-0.162671</td>
     </tr>
     <tr>
       <th>2</th>
@@ -1932,9 +1943,9 @@ scored_designs.head()
       <td>106</td>
       <td>ENST00000394249</td>
       <td>False</td>
-      <td>-0.063012</td>
-      <td>-0.106429</td>
-      <td>-0.283446</td>
+      <td>0.127005</td>
+      <td>0.083588</td>
+      <td>-0.093429</td>
     </tr>
     <tr>
       <th>3</th>
@@ -1956,9 +1967,9 @@ scored_designs.head()
       <td>263</td>
       <td>ENST00000394249</td>
       <td>False</td>
-      <td>-0.126357</td>
-      <td>0.632899</td>
-      <td>0.327112</td>
+      <td>-0.035591</td>
+      <td>0.723665</td>
+      <td>0.417878</td>
     </tr>
     <tr>
       <th>4</th>
@@ -1980,9 +1991,9 @@ scored_designs.head()
       <td>140</td>
       <td>ENST00000361337</td>
       <td>False</td>
-      <td>-0.234410</td>
-      <td>0.189591</td>
-      <td>-0.431445</td>
+      <td>-0.154246</td>
+      <td>0.269755</td>
+      <td>-0.351282</td>
     </tr>
   </tbody>
 </table>
@@ -2158,9 +2169,9 @@ gecko_activity_scores.head()
       <td>46</td>
       <td>ENST00000259457</td>
       <td>False</td>
-      <td>0.064880</td>
-      <td>-0.229246</td>
-      <td>-0.116557</td>
+      <td>0.018766</td>
+      <td>-0.275359</td>
+      <td>-0.162671</td>
     </tr>
     <tr>
       <th>1</th>
@@ -2182,9 +2193,9 @@ gecko_activity_scores.head()
       <td>106</td>
       <td>ENST00000394249</td>
       <td>False</td>
-      <td>-0.063012</td>
-      <td>-0.106429</td>
-      <td>-0.283446</td>
+      <td>0.127005</td>
+      <td>0.083588</td>
+      <td>-0.093429</td>
     </tr>
     <tr>
       <th>2</th>
@@ -2206,9 +2217,9 @@ gecko_activity_scores.head()
       <td>50</td>
       <td>ENST00000361337</td>
       <td>False</td>
-      <td>-0.354708</td>
-      <td>-0.648835</td>
-      <td>-0.377659</td>
+      <td>-0.137369</td>
+      <td>-0.431496</td>
+      <td>-0.160320</td>
     </tr>
     <tr>
       <th>3</th>
@@ -2230,9 +2241,9 @@ gecko_activity_scores.head()
       <td>34</td>
       <td>ENST00000368328</td>
       <td>False</td>
-      <td>0.129285</td>
-      <td>-0.538114</td>
-      <td>-0.179509</td>
+      <td>0.120044</td>
+      <td>-0.547355</td>
+      <td>-0.188750</td>
     </tr>
     <tr>
       <th>4</th>
@@ -2254,9 +2265,9 @@ gecko_activity_scores.head()
       <td>157</td>
       <td>ENST00000610426</td>
       <td>False</td>
-      <td>-0.113577</td>
-      <td>-0.515797</td>
-      <td>-0.736069</td>
+      <td>0.047223</td>
+      <td>-0.354996</td>
+      <td>-0.575268</td>
     </tr>
   </tbody>
 </table>
@@ -2302,34 +2313,38 @@ scored_designs
     Calculating sequence-based features
 
 
-    100%|██████████| 849/849 [00:06<00:00, 137.86it/s]
+    100%|██████████| 849/849 [00:00<00:00, 2397.41it/s]
 
 
     Calculating sequence-based features
 
 
-    100%|██████████| 849/849 [00:02<00:00, 321.44it/s]
+    100%|██████████| 849/849 [00:00<00:00, 3400.56it/s]
 
 
     Getting amino acid sequences
 
 
-    100%|██████████| 1/1 [00:00<00:00,  1.77it/s]
+    100%|██████████| 1/1 [00:00<00:00,  4.01it/s]
 
 
     Getting protein domains
 
 
-    100%|██████████| 3/3 [00:00<00:00, 899.29it/s]
+    100%|██████████| 3/3 [00:00<00:00, 2714.76it/s]
 
 
     Getting conservation
 
 
-    100%|██████████| 3/3 [00:00<00:00, 10.67it/s]
-    /opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+    100%|██████████| 3/3 [00:00<00:00, 29.72it/s]
+    /Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+    https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
+      warnings.warn(
+    /Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+    https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
       warnings.warn(
-    /opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+    /Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names
       warnings.warn(
 
 
@@ -2398,9 +2413,9 @@ scored_designs
       <td>666</td>
       <td>ENST00000309311</td>
       <td>False</td>
-      <td>-0.115549</td>
-      <td>0.792261</td>
-      <td>0.654408</td>
+      <td>-0.143972</td>
+      <td>0.763837</td>
+      <td>0.625984</td>
     </tr>
     <tr>
       <th>1</th>
@@ -2422,9 +2437,9 @@ scored_designs
       <td>581</td>
       <td>ENST00000309311</td>
       <td>False</td>
-      <td>-0.017643</td>
-      <td>0.154226</td>
-      <td>0.022776</td>
+      <td>-0.027326</td>
+      <td>0.144543</td>
+      <td>0.013093</td>
     </tr>
     <tr>
       <th>2</th>
@@ -2446,9 +2461,9 @@ scored_designs
       <td>107</td>
       <td>ENST00000309311</td>
       <td>False</td>
-      <td>0.172910</td>
-      <td>1.566422</td>
-      <td>0.750642</td>
+      <td>-0.002082</td>
+      <td>1.391430</td>
+      <td>0.575650</td>
     </tr>
     <tr>
       <th>3</th>
@@ -2470,9 +2485,9 @@ scored_designs
       <td>406</td>
       <td>ENST00000309311</td>
       <td>False</td>
-      <td>0.121034</td>
-      <td>1.025480</td>
-      <td>0.129424</td>
+      <td>0.080430</td>
+      <td>0.984876</td>
+      <td>0.088820</td>
     </tr>
     <tr>
       <th>4</th>
@@ -2494,9 +2509,9 @@ scored_designs
       <td>546</td>
       <td>ENST00000309311</td>
       <td>False</td>
-      <td>0.036041</td>
-      <td>0.867128</td>
-      <td>0.397635</td>
+      <td>0.025749</td>
+      <td>0.856836</td>
+      <td>0.387343</td>
     </tr>
     <tr>
       <th>...</th>
@@ -2542,9 +2557,9 @@ scored_designs
       <td>52</td>
       <td>ENST00000369026</td>
       <td>False</td>
-      <td>-0.299583</td>
-      <td>-1.092501</td>
-      <td>-0.963464</td>
+      <td>-0.136403</td>
+      <td>-0.929321</td>
+      <td>-0.800284</td>
     </tr>
     <tr>
       <th>845</th>
@@ -2590,9 +2605,9 @@ scored_designs
       <td>24</td>
       <td>ENST00000369026</td>
       <td>False</td>
-      <td>-0.285485</td>
-      <td>-1.386788</td>
-      <td>-1.581125</td>
+      <td>-0.148665</td>
+      <td>-1.249969</td>
+      <td>-1.444305</td>
     </tr>
     <tr>
       <th>847</th>
@@ -2614,9 +2629,9 @@ scored_designs
       <td>30</td>
       <td>ENST00000369026</td>
       <td>False</td>
-      <td>-0.312348</td>
-      <td>-0.929779</td>
-      <td>-0.933784</td>
+      <td>-0.145388</td>
+      <td>-0.762819</td>
+      <td>-0.766825</td>
     </tr>
     <tr>
       <th>848</th>
@@ -2638,9 +2653,9 @@ scored_designs
       <td>30</td>
       <td>ENST00000369026</td>
       <td>False</td>
-      <td>-0.312348</td>
-      <td>-0.899159</td>
-      <td>-0.976478</td>
+      <td>-0.145388</td>
+      <td>-0.732200</td>
+      <td>-0.809518</td>
     </tr>
   </tbody>
 </table>
@@ -2649,6 +2664,30 @@ scored_designs
 
 
 
+```
+crispick_designs = pd.read_table('test_data/CRISPick_reference_designs.txt')
+```
+
+```
+merged_designs = (scored_designs
+                  .merge(crispick_designs[['sgRNA Cut Position (1-based)',	'sgRNA Sequence', 'On-Target Efficacy Score']]
+                         .rename(columns={'On-Target Efficacy Score': 'CRISPick_score'}), how='inner', 
+                         on=['sgRNA Cut Position (1-based)',	'sgRNA Sequence']))
+```
+
+```
+plt.subplots(figsize=(4,4))
+gpplot.add_correlation(data=merged_designs, x='CRISPick_score', y='RS3 Sequence (Chen2013 tracr) + Target Score')
+sns.scatterplot(data=merged_designs, x='CRISPick_score', y='RS3 Sequence (Chen2013 tracr) + Target Score')
+sns.despine()
+```
+
+
+    
+![png](docs/images/output_48_0.png)
+    
+
+
 We see that the predict function is querying the target data in addition
 to making predictions.
 
diff --git a/docs/images/output_42_0.png b/docs/images/output_42_0.png
index ce5344f..7ba04b5 100644
Binary files a/docs/images/output_42_0.png and b/docs/images/output_42_0.png differ
diff --git a/docs/images/output_48_0.png b/docs/images/output_48_0.png
new file mode 100644
index 0000000..b545de3
Binary files /dev/null and b/docs/images/output_48_0.png differ
diff --git a/docs/index.html b/docs/index.html
index 6a0e696..35dc5c9 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -70,7 +70,7 @@ <h2 id="Quick-Start">Quick Start<a class="anchor-link" href="#Quick-Start"> </a>
 <div class="input">
 <div class="inner_cell">
 <div class="input_area">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rs3.seq</span> <span class="kn">import</span> <span class="n">predict_seq</span>
+<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">rs3.seq</span><span class="w"> </span><span class="kn">import</span> <span class="n">predict_seq</span>
 </pre></div>
 </div>
 </div>
@@ -129,7 +129,7 @@ <h2 id="Quick-Start">Quick Start<a class="anchor-link" href="#Quick-Start"> </a>
 </div>
 <div class="output_area">
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████| 2/2 [00:00&lt;00:00, 15.04it/s]
+<pre>100%|██████████| 2/2 [00:00&lt;00:00, 352.28it/s]
 </pre>
 </div>
 </div>
@@ -157,9 +157,9 @@ <h3 id="Target-based-model">Target based model<a class="anchor-link" href="#Targ
 <div class="input">
 <div class="inner_cell">
 <div class="input_area">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">from</span> <span class="nn">rs3.predicttarg</span> <span class="kn">import</span> <span class="n">predict_target</span>
-<span class="kn">from</span> <span class="nn">rs3.targetfeat</span> <span class="kn">import</span> <span class="p">(</span><span class="n">add_target_columns</span><span class="p">,</span>
+<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">rs3.predicttarg</span><span class="w"> </span><span class="kn">import</span> <span class="n">predict_target</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">rs3.targetfeat</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span><span class="n">add_target_columns</span><span class="p">,</span>
                             <span class="n">get_aa_subseq_df</span><span class="p">,</span>
                             <span class="n">get_protein_domain_features</span><span class="p">,</span>
                             <span class="n">get_conservation_features</span><span class="p">)</span>
@@ -776,9 +776,9 @@ <h4 id="Amino-acid-sequence-input">Amino acid sequence input<a class="anchor-lin
 <th>id</th>
 <th>AA len</th>
 <th>Target Cut Length</th>
+<th>AA Index</th>
 <th>Orientation</th>
 <th>sgRNA Context Sequence</th>
-<th>AA Index</th>
 <th>extended_seq</th>
 <th>AA 0-Indexed</th>
 <th>AA 0-Indexed padded</th>
@@ -800,9 +800,9 @@ <h4 id="Amino-acid-sequence-input">Amino acid sequence input<a class="anchor-lin
 <td>ENSP00000259457</td>
 <td>277</td>
 <td>191</td>
+<td>64</td>
 <td>sense</td>
 <td>TGGAGCAGATACAAGAGCAACTGAAGGGAT</td>
-<td>64</td>
 <td>-----------------MAAVSVYAPPVGGFSFDNCRRNAVLEADF...</td>
 <td>63</td>
 <td>80</td>
@@ -822,9 +822,9 @@ <h4 id="Amino-acid-sequence-input">Amino acid sequence input<a class="anchor-lin
 <td>ENSP00000259457</td>
 <td>277</td>
 <td>137</td>
+<td>46</td>
 <td>sense</td>
 <td>CCGGAAAACTGGCACGACCATCGCTGGGGT</td>
-<td>46</td>
 <td>-----------------MAAVSVYAPPVGGFSFDNCRRNAVLEADF...</td>
 <td>45</td>
 <td>62</td>
@@ -844,9 +844,9 @@ <h4 id="Amino-acid-sequence-input">Amino acid sequence input<a class="anchor-lin
 <td>ENSP00000377793</td>
 <td>620</td>
 <td>316</td>
+<td>106</td>
 <td>sense</td>
 <td>TAGAAAAAGATTTGCGCACCCAAGTGGAAT</td>
-<td>106</td>
 <td>-----------------MRRSEVLAEESIVCLQKALNHLREIWELI...</td>
 <td>105</td>
 <td>122</td>
@@ -866,9 +866,9 @@ <h4 id="Amino-acid-sequence-input">Amino acid sequence input<a class="anchor-lin
 <td>ENSP00000377793</td>
 <td>620</td>
 <td>787</td>
+<td>263</td>
 <td>antisense</td>
 <td>TGGCCTTTGACCCAGACATAATGGTGGCCA</td>
-<td>263</td>
 <td>-----------------MRRSEVLAEESIVCLQKALNHLREIWELI...</td>
 <td>262</td>
 <td>279</td>
@@ -888,9 +888,9 @@ <h4 id="Amino-acid-sequence-input">Amino acid sequence input<a class="anchor-lin
 <td>ENSP00000354522</td>
 <td>765</td>
 <td>420</td>
+<td>140</td>
 <td>antisense</td>
 <td>AAATACTCACTCATCCTCATCTCGAGGTCT</td>
-<td>140</td>
 <td>-----------------MSGDHLHNDSQIEADFRLNDSHKHKDKHK...</td>
 <td>139</td>
 <td>156</td>
@@ -932,9 +932,13 @@ <h4 id="Lite-Scores">Lite Scores<a class="anchor-link" href="#Lite-Scores"> </a>
 <div class="output">
 <div class="output_area">
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+<pre>/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
+  warnings.warn(
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
   warnings.warn(
-/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names
   warnings.warn(
 </pre>
 </div>
@@ -1004,7 +1008,7 @@ <h4 id="Lite-Scores">Lite Scores<a class="anchor-link" href="#Lite-Scores"> </a>
 <td>1</td>
 <td>0</td>
 <td>Preselected</td>
-<td>0.012467</td>
+<td>-0.088666</td>
 </tr>
 <tr>
 <th>1</th>
@@ -1028,7 +1032,7 @@ <h4 id="Lite-Scores">Lite Scores<a class="anchor-link" href="#Lite-Scores"> </a>
 <td>2</td>
 <td>0</td>
 <td>Preselected</td>
-<td>0.048338</td>
+<td>-0.013821</td>
 </tr>
 <tr>
 <th>2</th>
@@ -1052,7 +1056,7 @@ <h4 id="Lite-Scores">Lite Scores<a class="anchor-link" href="#Lite-Scores"> </a>
 <td>1</td>
 <td>0</td>
 <td>Preselected</td>
-<td>-0.129234</td>
+<td>0.103036</td>
 </tr>
 <tr>
 <th>3</th>
@@ -1076,7 +1080,7 @@ <h4 id="Lite-Scores">Lite Scores<a class="anchor-link" href="#Lite-Scores"> </a>
 <td>2</td>
 <td>0</td>
 <td>Preselected</td>
-<td>0.061647</td>
+<td>0.160578</td>
 </tr>
 <tr>
 <th>4</th>
@@ -1100,7 +1104,7 @@ <h4 id="Lite-Scores">Lite Scores<a class="anchor-link" href="#Lite-Scores"> </a>
 <td>2</td>
 <td>1</td>
 <td>NaN</td>
-<td>-0.009100</td>
+<td>0.069597</td>
 </tr>
 </tbody>
 </table>
@@ -1812,9 +1816,13 @@ <h4 id="Full-Target-Scores">Full Target Scores<a class="anchor-link" href="#Full
 <div class="output">
 <div class="output_area">
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+<pre>/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
   warnings.warn(
-/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
+  warnings.warn(
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names
   warnings.warn(
 </pre>
 </div>
@@ -1883,8 +1891,8 @@ <h4 id="Full-Target-Scores">Full Target Scores<a class="anchor-link" href="#Full
 <td>1</td>
 <td>0</td>
 <td>Preselected</td>
-<td>0.012467</td>
-<td>0.152037</td>
+<td>-0.088666</td>
+<td>0.110229</td>
 </tr>
 <tr>
 <th>1</th>
@@ -1907,8 +1915,8 @@ <h4 id="Full-Target-Scores">Full Target Scores<a class="anchor-link" href="#Full
 <td>2</td>
 <td>0</td>
 <td>Preselected</td>
-<td>0.048338</td>
-<td>0.064880</td>
+<td>-0.013821</td>
+<td>0.018766</td>
 </tr>
 <tr>
 <th>2</th>
@@ -1931,8 +1939,8 @@ <h4 id="Full-Target-Scores">Full Target Scores<a class="anchor-link" href="#Full
 <td>1</td>
 <td>0</td>
 <td>Preselected</td>
-<td>-0.129234</td>
-<td>-0.063012</td>
+<td>0.103036</td>
+<td>0.127005</td>
 </tr>
 <tr>
 <th>3</th>
@@ -1955,8 +1963,8 @@ <h4 id="Full-Target-Scores">Full Target Scores<a class="anchor-link" href="#Full
 <td>2</td>
 <td>0</td>
 <td>Preselected</td>
-<td>0.061647</td>
-<td>-0.126357</td>
+<td>0.160578</td>
+<td>-0.035591</td>
 </tr>
 <tr>
 <th>4</th>
@@ -1979,8 +1987,8 @@ <h4 id="Full-Target-Scores">Full Target Scores<a class="anchor-link" href="#Full
 <td>2</td>
 <td>1</td>
 <td>NaN</td>
-<td>-0.009100</td>
-<td>-0.234410</td>
+<td>0.069597</td>
+<td>-0.154246</td>
 </tr>
 </tbody>
 </table>
@@ -2006,10 +2014,10 @@ <h3 id="Predict-Function">Predict Function<a class="anchor-link" href="#Predict-
 <div class="input">
 <div class="inner_cell">
 <div class="input_area">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rs3.predict</span> <span class="kn">import</span> <span class="n">predict</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">gpplot</span>
-<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">rs3.predict</span><span class="w"> </span><span class="kn">import</span> <span class="n">predict</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">gpplot</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">seaborn</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">sns</span>
 </pre></div>
 </div>
 </div>
@@ -2038,7 +2046,7 @@ <h4 id="Preloaded-data">Preloaded data<a class="anchor-link" href="#Preloaded-da
 <div class="inner_cell">
 <div class="input_area">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">design_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">'test_data/sgrna-designs.txt'</span><span class="p">)</span>
-<span class="kn">import</span> <span class="nn">multiprocessing</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">multiprocessing</span>
 <span class="n">max_n_jobs</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span>
 </pre></div>
 </div>
@@ -2074,7 +2082,7 @@ <h4 id="Preloaded-data">Preloaded data<a class="anchor-link" href="#Preloaded-da
 </div>
 <div class="output_area">
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████| 400/400 [00:05&lt;00:00, 68.98it/s] 
+<pre>100%|██████████| 400/400 [00:01&lt;00:00, 226.77it/s]
 </pre>
 </div>
 </div>
@@ -2086,10 +2094,14 @@ <h4 id="Preloaded-data">Preloaded data<a class="anchor-link" href="#Preloaded-da
 </div>
 <div class="output_area">
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████| 400/400 [00:01&lt;00:00, 229.85it/s]
-/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+<pre>100%|██████████| 400/400 [00:00&lt;00:00, 2734.69it/s]
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
+  warnings.warn(
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
   warnings.warn(
-/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names
   warnings.warn(
 </pre>
 </div>
@@ -2157,9 +2169,9 @@ <h4 id="Preloaded-data">Preloaded data<a class="anchor-link" href="#Preloaded-da
 <td>64</td>
 <td>ENST00000259457</td>
 <td>False</td>
-<td>0.152037</td>
-<td>0.939676</td>
-<td>0.711381</td>
+<td>0.110229</td>
+<td>0.897869</td>
+<td>0.669574</td>
 </tr>
 <tr>
 <th>1</th>
@@ -2181,9 +2193,9 @@ <h4 id="Preloaded-data">Preloaded data<a class="anchor-link" href="#Preloaded-da
 <td>46</td>
 <td>ENST00000259457</td>
 <td>False</td>
-<td>0.064880</td>
-<td>-0.229246</td>
-<td>-0.116557</td>
+<td>0.018766</td>
+<td>-0.275359</td>
+<td>-0.162671</td>
 </tr>
 <tr>
 <th>2</th>
@@ -2205,9 +2217,9 @@ <h4 id="Preloaded-data">Preloaded data<a class="anchor-link" href="#Preloaded-da
 <td>106</td>
 <td>ENST00000394249</td>
 <td>False</td>
-<td>-0.063012</td>
-<td>-0.106429</td>
-<td>-0.283446</td>
+<td>0.127005</td>
+<td>0.083588</td>
+<td>-0.093429</td>
 </tr>
 <tr>
 <th>3</th>
@@ -2229,9 +2241,9 @@ <h4 id="Preloaded-data">Preloaded data<a class="anchor-link" href="#Preloaded-da
 <td>263</td>
 <td>ENST00000394249</td>
 <td>False</td>
-<td>-0.126357</td>
-<td>0.632899</td>
-<td>0.327112</td>
+<td>-0.035591</td>
+<td>0.723665</td>
+<td>0.417878</td>
 </tr>
 <tr>
 <th>4</th>
@@ -2253,9 +2265,9 @@ <h4 id="Preloaded-data">Preloaded data<a class="anchor-link" href="#Preloaded-da
 <td>140</td>
 <td>ENST00000361337</td>
 <td>False</td>
-<td>-0.234410</td>
-<td>0.189591</td>
-<td>-0.431445</td>
+<td>-0.154246</td>
+<td>0.269755</td>
+<td>-0.351282</td>
 </tr>
 </tbody>
 </table>
@@ -2454,9 +2466,9 @@ <h4 id="Preloaded-data">Preloaded data<a class="anchor-link" href="#Preloaded-da
 <td>46</td>
 <td>ENST00000259457</td>
 <td>False</td>
-<td>0.064880</td>
-<td>-0.229246</td>
-<td>-0.116557</td>
+<td>0.018766</td>
+<td>-0.275359</td>
+<td>-0.162671</td>
 </tr>
 <tr>
 <th>1</th>
@@ -2478,9 +2490,9 @@ <h4 id="Preloaded-data">Preloaded data<a class="anchor-link" href="#Preloaded-da
 <td>106</td>
 <td>ENST00000394249</td>
 <td>False</td>
-<td>-0.063012</td>
-<td>-0.106429</td>
-<td>-0.283446</td>
+<td>0.127005</td>
+<td>0.083588</td>
+<td>-0.093429</td>
 </tr>
 <tr>
 <th>2</th>
@@ -2502,9 +2514,9 @@ <h4 id="Preloaded-data">Preloaded data<a class="anchor-link" href="#Preloaded-da
 <td>50</td>
 <td>ENST00000361337</td>
 <td>False</td>
-<td>-0.354708</td>
-<td>-0.648835</td>
-<td>-0.377659</td>
+<td>-0.137369</td>
+<td>-0.431496</td>
+<td>-0.160320</td>
 </tr>
 <tr>
 <th>3</th>
@@ -2526,9 +2538,9 @@ <h4 id="Preloaded-data">Preloaded data<a class="anchor-link" href="#Preloaded-da
 <td>34</td>
 <td>ENST00000368328</td>
 <td>False</td>
-<td>0.129285</td>
-<td>-0.538114</td>
-<td>-0.179509</td>
+<td>0.120044</td>
+<td>-0.547355</td>
+<td>-0.188750</td>
 </tr>
 <tr>
 <th>4</th>
@@ -2550,9 +2562,9 @@ <h4 id="Preloaded-data">Preloaded data<a class="anchor-link" href="#Preloaded-da
 <td>157</td>
 <td>ENST00000610426</td>
 <td>False</td>
-<td>-0.113577</td>
-<td>-0.515797</td>
-<td>-0.736069</td>
+<td>0.047223</td>
+<td>-0.354996</td>
+<td>-0.575268</td>
 </tr>
 </tbody>
 </table>
@@ -2590,7 +2602,7 @@ <h4 id="Preloaded-data">Preloaded data<a class="anchor-link" href="#Preloaded-da
 <div class="output">
 <div class="output_area">
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-<img alt="No description has been provided for this image" 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"/>
+<img alt="No description has been provided for this image" 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"/>
 </div>
 </div>
 </div>
@@ -2644,7 +2656,7 @@ <h4 id="Predictions-on-the-fly">Predictions on the fly<a class="anchor-link" hre
 </div>
 <div class="output_area">
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████| 849/849 [00:06&lt;00:00, 137.86it/s]
+<pre>100%|██████████| 849/849 [00:00&lt;00:00, 2397.41it/s]
 </pre>
 </div>
 </div>
@@ -2656,7 +2668,7 @@ <h4 id="Predictions-on-the-fly">Predictions on the fly<a class="anchor-link" hre
 </div>
 <div class="output_area">
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████| 849/849 [00:02&lt;00:00, 321.44it/s]
+<pre>100%|██████████| 849/849 [00:00&lt;00:00, 3400.56it/s]
 </pre>
 </div>
 </div>
@@ -2668,7 +2680,7 @@ <h4 id="Predictions-on-the-fly">Predictions on the fly<a class="anchor-link" hre
 </div>
 <div class="output_area">
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████| 1/1 [00:00&lt;00:00,  1.77it/s]
+<pre>100%|██████████| 1/1 [00:00&lt;00:00,  4.01it/s]
 </pre>
 </div>
 </div>
@@ -2680,7 +2692,7 @@ <h4 id="Predictions-on-the-fly">Predictions on the fly<a class="anchor-link" hre
 </div>
 <div class="output_area">
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████| 3/3 [00:00&lt;00:00, 899.29it/s]
+<pre>100%|██████████| 3/3 [00:00&lt;00:00, 2714.76it/s]
 </pre>
 </div>
 </div>
@@ -2692,10 +2704,14 @@ <h4 id="Predictions-on-the-fly">Predictions on the fly<a class="anchor-link" hre
 </div>
 <div class="output_area">
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████| 3/3 [00:00&lt;00:00, 10.67it/s]
-/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+<pre>100%|██████████| 3/3 [00:00&lt;00:00, 29.72it/s]
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
   warnings.warn(
-/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
+  warnings.warn(
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names
   warnings.warn(
 </pre>
 </div>
@@ -2763,9 +2779,9 @@ <h4 id="Predictions-on-the-fly">Predictions on the fly<a class="anchor-link" hre
 <td>666</td>
 <td>ENST00000309311</td>
 <td>False</td>
-<td>-0.115549</td>
-<td>0.792261</td>
-<td>0.654408</td>
+<td>-0.143972</td>
+<td>0.763837</td>
+<td>0.625984</td>
 </tr>
 <tr>
 <th>1</th>
@@ -2787,9 +2803,9 @@ <h4 id="Predictions-on-the-fly">Predictions on the fly<a class="anchor-link" hre
 <td>581</td>
 <td>ENST00000309311</td>
 <td>False</td>
-<td>-0.017643</td>
-<td>0.154226</td>
-<td>0.022776</td>
+<td>-0.027326</td>
+<td>0.144543</td>
+<td>0.013093</td>
 </tr>
 <tr>
 <th>2</th>
@@ -2811,9 +2827,9 @@ <h4 id="Predictions-on-the-fly">Predictions on the fly<a class="anchor-link" hre
 <td>107</td>
 <td>ENST00000309311</td>
 <td>False</td>
-<td>0.172910</td>
-<td>1.566422</td>
-<td>0.750642</td>
+<td>-0.002082</td>
+<td>1.391430</td>
+<td>0.575650</td>
 </tr>
 <tr>
 <th>3</th>
@@ -2835,9 +2851,9 @@ <h4 id="Predictions-on-the-fly">Predictions on the fly<a class="anchor-link" hre
 <td>406</td>
 <td>ENST00000309311</td>
 <td>False</td>
-<td>0.121034</td>
-<td>1.025480</td>
-<td>0.129424</td>
+<td>0.080430</td>
+<td>0.984876</td>
+<td>0.088820</td>
 </tr>
 <tr>
 <th>4</th>
@@ -2859,9 +2875,9 @@ <h4 id="Predictions-on-the-fly">Predictions on the fly<a class="anchor-link" hre
 <td>546</td>
 <td>ENST00000309311</td>
 <td>False</td>
-<td>0.036041</td>
-<td>0.867128</td>
-<td>0.397635</td>
+<td>0.025749</td>
+<td>0.856836</td>
+<td>0.387343</td>
 </tr>
 <tr>
 <th>...</th>
@@ -2907,9 +2923,9 @@ <h4 id="Predictions-on-the-fly">Predictions on the fly<a class="anchor-link" hre
 <td>52</td>
 <td>ENST00000369026</td>
 <td>False</td>
-<td>-0.299583</td>
-<td>-1.092501</td>
-<td>-0.963464</td>
+<td>-0.136403</td>
+<td>-0.929321</td>
+<td>-0.800284</td>
 </tr>
 <tr>
 <th>845</th>
@@ -2955,9 +2971,9 @@ <h4 id="Predictions-on-the-fly">Predictions on the fly<a class="anchor-link" hre
 <td>24</td>
 <td>ENST00000369026</td>
 <td>False</td>
-<td>-0.285485</td>
-<td>-1.386788</td>
-<td>-1.581125</td>
+<td>-0.148665</td>
+<td>-1.249969</td>
+<td>-1.444305</td>
 </tr>
 <tr>
 <th>847</th>
@@ -2979,9 +2995,9 @@ <h4 id="Predictions-on-the-fly">Predictions on the fly<a class="anchor-link" hre
 <td>30</td>
 <td>ENST00000369026</td>
 <td>False</td>
-<td>-0.312348</td>
-<td>-0.929779</td>
-<td>-0.933784</td>
+<td>-0.145388</td>
+<td>-0.762819</td>
+<td>-0.766825</td>
 </tr>
 <tr>
 <th>848</th>
@@ -3003,9 +3019,9 @@ <h4 id="Predictions-on-the-fly">Predictions on the fly<a class="anchor-link" hre
 <td>30</td>
 <td>ENST00000369026</td>
 <td>False</td>
-<td>-0.312348</td>
-<td>-0.899159</td>
-<td>-0.976478</td>
+<td>-0.145388</td>
+<td>-0.732200</td>
+<td>-0.809518</td>
 </tr>
 </tbody>
 </table>
@@ -3014,6 +3030,63 @@ <h4 id="Predictions-on-the-fly">Predictions on the fly<a class="anchor-link" hre
 </div>
 </div>
 </div>
+</div>
+    {% endraw %}
+
+    {% raw %}
+    
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="inner_cell">
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">crispick_designs</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">'test_data/CRISPick_reference_designs.txt'</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+    {% endraw %}
+
+    {% raw %}
+    
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="inner_cell">
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">merged_designs</span> <span class="o">=</span> <span class="p">(</span><span class="n">scored_designs</span>
+                  <span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">crispick_designs</span><span class="p">[[</span><span class="s1">'sgRNA Cut Position (1-based)'</span><span class="p">,</span>	<span class="s1">'sgRNA Sequence'</span><span class="p">,</span> <span class="s1">'On-Target Efficacy Score'</span><span class="p">]]</span>
+                         <span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s1">'On-Target Efficacy Score'</span><span class="p">:</span> <span class="s1">'CRISPick_score'</span><span class="p">}),</span> <span class="n">how</span><span class="o">=</span><span class="s1">'inner'</span><span class="p">,</span> 
+                         <span class="n">on</span><span class="o">=</span><span class="p">[</span><span class="s1">'sgRNA Cut Position (1-based)'</span><span class="p">,</span>	<span class="s1">'sgRNA Sequence'</span><span class="p">]))</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+    {% endraw %}
+
+    {% raw %}
+    
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="inner_cell">
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
+<span class="n">gpplot</span><span class="o">.</span><span class="n">add_correlation</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">merged_designs</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s1">'CRISPick_score'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">'RS3 Sequence (Chen2013 tracr) + Target Score'</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">scatterplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">merged_designs</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s1">'CRISPick_score'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">'RS3 Sequence (Chen2013 tracr) + Target Score'</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">despine</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output">
+<div class="output_area">
+<div class="output_png output_subarea">
+<img alt="No description has been provided for this image" 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"/>
+</div>
+</div>
+</div>
+</div>
 </div>
     {% endraw %}
 
diff --git a/docs/predict.html b/docs/predict.html
index 062e245..0b493bb 100644
--- a/docs/predict.html
+++ b/docs/predict.html
@@ -19,20 +19,17 @@
 # command to build the docs after a change: nbdev_build_docs
 
 -->
-
 <div class="container" id="notebook-container">
         
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 </div>
     {% endraw %}
 
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 </div>
     {% endraw %}
 
@@ -40,16 +37,13 @@
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rs3.targetdata</span> <span class="kn">import</span> <span class="n">write_transcript_data</span><span class="p">,</span> <span class="n">write_conservation_data</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">rs3.targetdata</span><span class="w"> </span><span class="kn">import</span> <span class="n">write_transcript_data</span><span class="p">,</span> <span class="n">write_conservation_data</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 </div>
     {% endraw %}
 
@@ -57,83 +51,66 @@
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">design_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">&#39;test_data/sgrna-designs.txt&#39;</span><span class="p">)</span>
-<span class="n">sanger_activity</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;test_data/Behan2019_activity.csv&#39;</span><span class="p">)</span>
-<span class="n">gecko_activity</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;test_data/Aguirre2016_activity.csv&#39;</span><span class="p">)</span>
-<span class="kn">import</span> <span class="nn">multiprocessing</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">design_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">'test_data/sgrna-designs.txt'</span><span class="p">)</span>
+<span class="n">sanger_activity</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'test_data/Behan2019_activity.csv'</span><span class="p">)</span>
+<span class="n">gecko_activity</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'test_data/Aguirre2016_activity.csv'</span><span class="p">)</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">multiprocessing</span>
 <span class="n">max_n_jobs</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 </div>
     {% endraw %}
 
     {% raw %}
     
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-
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-<h4 id="predict_seq_tracr" class="doc_header"><code>predict_seq_tracr</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/predict.py#L22" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>predict_seq_tracr</code>(<strong><code>design_df</code></strong>, <strong><code>tracr</code></strong>, <strong><code>context_col</code></strong>, <strong><code>ref_tracrs</code></strong>, <strong><code>n_jobs</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="predict_seq_tracr"><code>predict_seq_tracr</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/predict.py#L22" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>predict_seq_tracr</code>(<strong><code>design_df</code></strong>, <strong><code>tracr</code></strong>, <strong><code>context_col</code></strong>, <strong><code>ref_tracrs</code></strong>, <strong><code>n_jobs</code></strong>)</p>
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-<h4 id="combine_target_seq_scores" class="doc_header"><code>combine_target_seq_scores</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/predict.py#L28" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>combine_target_seq_scores</code>(<strong><code>design_df</code></strong>, <strong><code>tracr</code></strong>, <strong><code>target_score_col</code></strong>, <strong><code>lite</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="combine_target_seq_scores"><code>combine_target_seq_scores</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/predict.py#L28" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>combine_target_seq_scores</code>(<strong><code>design_df</code></strong>, <strong><code>tracr</code></strong>, <strong><code>target_score_col</code></strong>, <strong><code>lite</code></strong>)</p>
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-<h4 id="predict" class="doc_header"><code>predict</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/predict.py#L36" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>predict</code>(<strong><code>design_df</code></strong>, <strong><code>tracr</code></strong>=<em><code>None</code></em>, <strong><code>target</code></strong>=<em><code>False</code></em>, <strong><code>aa_seq_file</code></strong>=<em><code>None</code></em>, <strong><code>domain_file</code></strong>=<em><code>None</code></em>, <strong><code>conservatin_file</code></strong>=<em><code>None</code></em>, <strong><code>id_cols</code></strong>=<em><code>None</code></em>, <strong><code>context_col</code></strong>=<em><code>'sgRNA Context Sequence'</code></em>, <strong><code>transcript_id_col</code></strong>=<em><code>'Target Transcript'</code></em>, <strong><code>transcript_base_col</code></strong>=<em><code>'Transcript Base'</code></em>, <strong><code>transcript_len_col</code></strong>=<em><code>'Target Total Length'</code></em>, <strong><code>n_jobs_min</code></strong>=<em><code>1</code></em>, <strong><code>n_jobs_max</code></strong>=<em><code>1</code></em>, <strong><code>lite</code></strong>=<em><code>True</code></em>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="predict"><code>predict</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/predict.py#L36" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>predict</code>(<strong><code>design_df</code></strong>, <strong><code>tracr</code></strong>=<em><code>None</code></em>, <strong><code>target</code></strong>=<em><code>False</code></em>, <strong><code>aa_seq_file</code></strong>=<em><code>None</code></em>, <strong><code>domain_file</code></strong>=<em><code>None</code></em>, <strong><code>conservatin_file</code></strong>=<em><code>None</code></em>, <strong><code>id_cols</code></strong>=<em><code>None</code></em>, <strong><code>context_col</code></strong>=<em><code>'sgRNA Context Sequence'</code></em>, <strong><code>transcript_id_col</code></strong>=<em><code>'Target Transcript'</code></em>, <strong><code>transcript_base_col</code></strong>=<em><code>'Transcript Base'</code></em>, <strong><code>transcript_len_col</code></strong>=<em><code>'Target Total Length'</code></em>, <strong><code>n_jobs_min</code></strong>=<em><code>1</code></em>, <strong><code>n_jobs_max</code></strong>=<em><code>1</code></em>, <strong><code>lite</code></strong>=<em><code>True</code></em>)</p>
 </blockquote>
 <p>Make predictions using RS3</p>
 <p>:param design_df: DataFrame
@@ -148,21 +125,16 @@ <h4 id="predict" class="doc_header"><code>predict</code><a href="https://github.
 :param transcript_len_col: str
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@@ -170,108 +142,95 @@ <h4 id="predict" class="doc_header"><code>predict</code><a href="https://github.
     
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">scored_designs</span> <span class="o">=</span> <span class="n">predict</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">tracr</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Hsu2013&#39;</span><span class="p">,</span> <span class="s1">&#39;Chen2013&#39;</span><span class="p">],</span> <span class="n">target</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">scored_designs</span> <span class="o">=</span> <span class="n">predict</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">tracr</span><span class="o">=</span><span class="p">[</span><span class="s1">'Hsu2013'</span><span class="p">,</span> <span class="s1">'Chen2013'</span><span class="p">],</span> <span class="n">target</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                          <span class="n">n_jobs_min</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_jobs_max</span><span class="o">=</span><span class="n">max_n_jobs</span><span class="p">,</span>
                          <span class="n">lite</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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-<pre>100%|██████████| 400/400 [00:04&lt;00:00, 85.01it/s] 
+<pre>100%|██████████| 400/400 [00:00&lt;00:00, 410.28it/s]
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-<pre>100%|██████████| 400/400 [00:00&lt;00:00, 2471.62it/s]
+<pre>100%|██████████| 400/400 [00:00&lt;00:00, 4219.77it/s]
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-<pre>100%|██████████| 4/4 [00:08&lt;00:00,  2.25s/it]
+<pre>100%|██████████| 4/4 [00:02&lt;00:00,  1.34it/s]
+/Users/peterdeweirdt/Documents/rs3/rs3/targetdata.py:111: UserWarning: Unable to find translations for the following transcripts: ENST00000629399, ENST00000284049
+  warnings.warn('Unable to find translations for the following transcripts: ' + ', '.join(missing_seqs))
+/var/folders/n9/c397fj3n5f16khr7tp76tbpc0000gn/T/ipykernel_38619/2834578737.py:67: UserWarning: Missing amino acid sequences for transcripts: ENST00000629399,ENST00000284049
+  warnings.warn('Missing amino acid sequences for transcripts: ' +
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-<pre>100%|██████████| 200/200 [00:59&lt;00:00,  3.35it/s]
+<pre>100%|██████████| 198/198 [01:20&lt;00:00,  2.46it/s]
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-<pre>100%|██████████| 200/200 [03:36&lt;00:00,  1.08s/it]
-/Users/pdeweird/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+<pre>100%|██████████| 200/200 [04:44&lt;00:00,  1.42s/it]
+/Users/peterdeweirdt/Documents/rs3/rs3/targetdata.py:346: UserWarning: Failed to get conservation scores for 2 transcriptsENST00000629399, ENST00000284049
+  warnings.warn('Failed to get conservation scores for ' + str(len(failed_list)) +
+/var/folders/n9/c397fj3n5f16khr7tp76tbpc0000gn/T/ipykernel_38619/2834578737.py:92: UserWarning: Missing conservation scores for transcripts: ENST00000629399,ENST00000284049
+  warnings.warn('Missing conservation scores for transcripts: ' +
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
+  warnings.warn(
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
   warnings.warn(
-/Users/pdeweird/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names
   warnings.warn(
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@@ -279,57 +238,46 @@ <h4 id="predict" class="doc_header"><code>predict</code><a href="https://github.
     
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-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">write_transcript_data</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-                      <span class="n">filepath</span><span class="o">=</span><span class="s1">&#39;./data/target_data/&#39;</span><span class="p">,</span>
-                      <span class="n">aa_seq_name</span><span class="o">=</span><span class="s1">&#39;aa_seqs.pq&#39;</span><span class="p">,</span>
-                      <span class="n">protein_domain_name</span><span class="o">=</span><span class="s1">&#39;protein_domains.pq&#39;</span><span class="p">)</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">write_transcript_data</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+                      <span class="n">filepath</span><span class="o">=</span><span class="s1">'./data/target_data/'</span><span class="p">,</span>
+                      <span class="n">aa_seq_name</span><span class="o">=</span><span class="s1">'aa_seqs.pq'</span><span class="p">,</span>
+                      <span class="n">protein_domain_name</span><span class="o">=</span><span class="s1">'protein_domains.pq'</span><span class="p">)</span>
 </pre></div>
-
-    </div>
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+</div>
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 <pre>Getting amino acid sequences
 </pre>
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-<pre>100%|██████████| 4/4 [00:05&lt;00:00,  1.31s/it]
+<pre>100%|██████████| 4/4 [00:03&lt;00:00,  1.05it/s]
+/Users/peterdeweirdt/Documents/rs3/rs3/targetdata.py:111: UserWarning: Unable to find translations for the following transcripts: ENST00000629399, ENST00000284049
+  warnings.warn('Unable to find translations for the following transcripts: ' + ', '.join(missing_seqs))
 </pre>
 </div>
 </div>
-
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 <pre>Getting protein domains
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-<pre>100%|██████████| 200/200 [01:00&lt;00:00,  3.30it/s]
+<pre>100%|██████████| 198/198 [00:50&lt;00:00,  3.94it/s]
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@@ -337,40 +285,33 @@ <h4 id="predict" class="doc_header"><code>predict</code><a href="https://github.
     
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-
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-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">write_conservation_data</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">max_n_jobs</span><span class="p">,</span>
-                        <span class="n">filepath</span><span class="o">=</span><span class="s1">&#39;./data/target_data/&#39;</span><span class="p">,</span>
-                        <span class="n">cons_file_name</span><span class="o">=</span><span class="s1">&#39;conservation.pq&#39;</span><span class="p">)</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">write_conservation_data</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">max_n_jobs</span><span class="p">,</span>
+                        <span class="n">filepath</span><span class="o">=</span><span class="s1">'./data/target_data/'</span><span class="p">,</span>
+                        <span class="n">cons_file_name</span><span class="o">=</span><span class="s1">'conservation.pq'</span><span class="p">)</span>
 </pre></div>
-
-    </div>
 </div>
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+</div>
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 <pre>Getting conservation
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-
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-
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-<pre>100%|██████████| 200/200 [03:35&lt;00:00,  1.08s/it]
+<pre>100%|██████████| 200/200 [05:06&lt;00:00,  1.53s/it]
+/Users/peterdeweirdt/Documents/rs3/rs3/targetdata.py:346: UserWarning: Failed to get conservation scores for 2 transcriptsENST00000629399, ENST00000284049
+  warnings.warn('Failed to get conservation scores for ' + str(len(failed_list)) +
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     {% endraw %}
 
@@ -378,64 +319,59 @@ <h4 id="predict" class="doc_header"><code>predict</code><a href="https://github.
     
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-
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-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">scored_designs_stored</span> <span class="o">=</span> <span class="n">predict</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">tracr</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Hsu2013&#39;</span><span class="p">,</span> <span class="s1">&#39;Chen2013&#39;</span><span class="p">],</span> <span class="n">target</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">scored_designs_stored</span> <span class="o">=</span> <span class="n">predict</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">tracr</span><span class="o">=</span><span class="p">[</span><span class="s1">'Hsu2013'</span><span class="p">,</span> <span class="s1">'Chen2013'</span><span class="p">],</span> <span class="n">target</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                                 <span class="n">n_jobs_min</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_jobs_max</span><span class="o">=</span><span class="n">max_n_jobs</span><span class="p">,</span>
-                                <span class="n">aa_seq_file</span><span class="o">=</span><span class="s1">&#39;./data/target_data/aa_seqs.pq&#39;</span><span class="p">,</span>
-                                <span class="n">domain_file</span><span class="o">=</span><span class="s1">&#39;./data/target_data/protein_domains.pq&#39;</span><span class="p">,</span>
-                                <span class="n">conservatin_file</span><span class="o">=</span><span class="s1">&#39;./data/target_data/conservation.pq&#39;</span><span class="p">,</span>
+                                <span class="n">aa_seq_file</span><span class="o">=</span><span class="s1">'./data/target_data/aa_seqs.pq'</span><span class="p">,</span>
+                                <span class="n">domain_file</span><span class="o">=</span><span class="s1">'./data/target_data/protein_domains.pq'</span><span class="p">,</span>
+                                <span class="n">conservatin_file</span><span class="o">=</span><span class="s1">'./data/target_data/conservation.pq'</span><span class="p">,</span>
                                 <span class="n">lite</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 <span class="n">pd</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_frame_equal</span><span class="p">(</span><span class="n">scored_designs</span><span class="p">,</span> <span class="n">scored_designs_stored</span><span class="p">)</span>
 </pre></div>
-
-    </div>
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+</div>
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 <pre>Calculating sequence-based features
 </pre>
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-
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-
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-<pre>100%|██████████| 400/400 [00:00&lt;00:00, 628.94it/s]
+<pre>100%|██████████| 400/400 [00:00&lt;00:00, 2050.58it/s]
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 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████| 400/400 [00:00&lt;00:00, 2256.05it/s]
-/Users/pdeweird/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+<pre>100%|██████████| 400/400 [00:00&lt;00:00, 3244.59it/s]
+/var/folders/n9/c397fj3n5f16khr7tp76tbpc0000gn/T/ipykernel_38619/2834578737.py:67: UserWarning: Missing amino acid sequences for transcripts: ENST00000629399,ENST00000284049
+  warnings.warn('Missing amino acid sequences for transcripts: ' +
+/var/folders/n9/c397fj3n5f16khr7tp76tbpc0000gn/T/ipykernel_38619/2834578737.py:92: UserWarning: Missing conservation scores for transcripts: ENST00000629399,ENST00000284049
+  warnings.warn('Missing conservation scores for transcripts: ' +
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
+  warnings.warn(
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
   warnings.warn(
-/Users/pdeweird/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names
   warnings.warn(
 </pre>
 </div>
 </div>
-
 </div>
 </div>
-
 </div>
     {% endraw %}
 
@@ -443,23 +379,20 @@ <h4 id="predict" class="doc_header"><code>predict</code><a href="https://github.
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">activity_id_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;sgRNA Sequence&#39;</span><span class="p">,</span> <span class="s1">&#39;sgRNA Context Sequence&#39;</span><span class="p">,</span>
-                    <span class="s1">&#39;Target Gene Symbol&#39;</span><span class="p">,</span> <span class="s1">&#39;Target Cut %&#39;</span><span class="p">]</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">activity_id_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'sgRNA Sequence'</span><span class="p">,</span> <span class="s1">'sgRNA Context Sequence'</span><span class="p">,</span>
+                    <span class="s1">'Target Gene Symbol'</span><span class="p">,</span> <span class="s1">'Target Cut %'</span><span class="p">]</span>
 <span class="n">sanger_actvity_scores</span> <span class="o">=</span> <span class="p">(</span><span class="n">sanger_activity</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">scored_designs_stored</span><span class="p">,</span>
-                                               <span class="n">how</span><span class="o">=</span><span class="s1">&#39;inner&#39;</span><span class="p">,</span>
+                                               <span class="n">how</span><span class="o">=</span><span class="s1">'inner'</span><span class="p">,</span>
                                                <span class="n">on</span><span class="o">=</span><span class="n">activity_id_cols</span><span class="p">))</span>
 <span class="n">gecko_activity_scores</span> <span class="o">=</span> <span class="p">(</span><span class="n">gecko_activity</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">scored_designs_stored</span><span class="p">,</span>
-                                              <span class="n">how</span><span class="o">=</span><span class="s1">&#39;inner&#39;</span><span class="p">,</span>
+                                              <span class="n">how</span><span class="o">=</span><span class="s1">'inner'</span><span class="p">,</span>
                                               <span class="n">on</span><span class="o">=</span><span class="n">activity_id_cols</span><span class="p">))</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 </div>
     {% endraw %}
 
@@ -467,52 +400,35 @@ <h4 id="predict" class="doc_header"><code>predict</code><a href="https://github.
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
-<span class="n">activity_col</span> <span class="o">=</span> <span class="s1">&#39;avg_mean_centered_neg_lfc&#39;</span>
-<span class="n">score_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;RS3 Sequence Score (Hsu2013 tracr)&#39;</span><span class="p">,</span>
-              <span class="s1">&#39;RS3 Sequence Score (Chen2013 tracr)&#39;</span><span class="p">,</span>
-              <span class="s1">&#39;Target Score&#39;</span><span class="p">,</span>
-              <span class="s1">&#39;RS3 Sequence (Hsu2013 tracr) + Target Score&#39;</span><span class="p">,</span>
-              <span class="s1">&#39;RS3 Sequence (Chen2013 tracr) + Target Score&#39;</span><span class="p">]</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">seaborn</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">sns</span>
+<span class="n">activity_col</span> <span class="o">=</span> <span class="s1">'avg_mean_centered_neg_lfc'</span>
+<span class="n">score_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'RS3 Sequence Score (Hsu2013 tracr)'</span><span class="p">,</span>
+              <span class="s1">'RS3 Sequence Score (Chen2013 tracr)'</span><span class="p">,</span>
+              <span class="s1">'Target Score'</span><span class="p">,</span>
+              <span class="s1">'RS3 Sequence (Hsu2013 tracr) + Target Score'</span><span class="p">,</span>
+              <span class="s1">'RS3 Sequence (Chen2013 tracr) + Target Score'</span><span class="p">]</span>
 <span class="n">sanger_activity_cors</span> <span class="o">=</span> <span class="n">sanger_actvity_scores</span><span class="p">[[</span><span class="n">activity_col</span><span class="p">]</span> <span class="o">+</span> <span class="n">score_cols</span><span class="p">]</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
-<span class="n">sns</span><span class="o">.</span><span class="n">clustermap</span><span class="p">(</span><span class="n">sanger_activity_cors</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;RdBu_r&#39;</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">clustermap</span><span class="p">(</span><span class="n">sanger_activity_cors</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'RdBu_r'</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 <div class="output_wrapper">
 <div class="output">
-
 <div class="output_area">
-
-
-
 <div class="output_text output_subarea output_execute_result">
-<pre>&lt;seaborn.matrix.ClusterGrid at 0x7fc6d0223340&gt;</pre>
+<pre>&lt;seaborn.matrix.ClusterGrid at 0x2912713a0&gt;</pre>
 </div>
-
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-
 <div class="output_area">
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-
-
-<div class="output_png output_subarea ">
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"/>
 </div>
-
 </div>
-
 </div>
 </div>
-
 </div>
     {% endraw %}
 
@@ -520,20 +436,17 @@ <h4 id="predict" class="doc_header"><code>predict</code><a href="https://github.
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="p">(</span><span class="n">sanger_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">&#39;RS3 Sequence (Chen2013 tracr) + Target Score&#39;</span><span class="p">]</span> <span class="o">&gt;</span>
-        <span class="n">sanger_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">&#39;RS3 Sequence (Hsu2013 tracr) + Target Score&#39;</span><span class="p">]</span> <span class="o">&gt;</span>
-        <span class="n">sanger_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">&#39;RS3 Sequence Score (Chen2013 tracr)&#39;</span><span class="p">]</span> <span class="o">&gt;</span>
-        <span class="n">sanger_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">&#39;RS3 Sequence Score (Hsu2013 tracr)&#39;</span><span class="p">]</span> <span class="o">&gt;</span>
-        <span class="n">sanger_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">&#39;Target Score&#39;</span><span class="p">])</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="p">(</span><span class="n">sanger_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">'RS3 Sequence (Chen2013 tracr) + Target Score'</span><span class="p">]</span> <span class="o">&gt;</span>
+        <span class="n">sanger_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">'RS3 Sequence (Hsu2013 tracr) + Target Score'</span><span class="p">]</span> <span class="o">&gt;</span>
+        <span class="n">sanger_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">'RS3 Sequence Score (Chen2013 tracr)'</span><span class="p">]</span> <span class="o">&gt;</span>
+        <span class="n">sanger_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">'RS3 Sequence Score (Hsu2013 tracr)'</span><span class="p">]</span> <span class="o">&gt;</span>
+        <span class="n">sanger_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">'Target Score'</span><span class="p">])</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 </div>
     {% endraw %}
 
@@ -541,45 +454,28 @@ <h4 id="predict" class="doc_header"><code>predict</code><a href="https://github.
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">gecko_activity_cors</span> <span class="o">=</span> <span class="n">gecko_activity_scores</span><span class="p">[[</span><span class="n">activity_col</span><span class="p">]</span> <span class="o">+</span> <span class="n">score_cols</span><span class="p">]</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
-<span class="n">sns</span><span class="o">.</span><span class="n">clustermap</span><span class="p">(</span><span class="n">gecko_activity_cors</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;RdBu_r&#39;</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">gecko_activity_cors</span> <span class="o">=</span> <span class="n">gecko_activity_scores</span><span class="p">[[</span><span class="n">activity_col</span><span class="p">]</span> <span class="o">+</span> <span class="n">score_cols</span><span class="p">]</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">clustermap</span><span class="p">(</span><span class="n">gecko_activity_cors</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'RdBu_r'</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 <div class="output_wrapper">
 <div class="output">
-
 <div class="output_area">
-
-
-
 <div class="output_text output_subarea output_execute_result">
-<pre>&lt;seaborn.matrix.ClusterGrid at 0x7fc6cfa528e0&gt;</pre>
+<pre>&lt;seaborn.matrix.ClusterGrid at 0x28a0b13d0&gt;</pre>
 </div>
-
 </div>
-
 <div class="output_area">
-
-
-
-<div class="output_png output_subarea ">
-<img 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"/>
 </div>
-
 </div>
-
 </div>
 </div>
-
 </div>
     {% endraw %}
 
@@ -587,23 +483,18 @@ <h4 id="predict" class="doc_header"><code>predict</code><a href="https://github.
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="p">(</span><span class="n">gecko_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">&#39;RS3 Sequence (Hsu2013 tracr) + Target Score&#39;</span><span class="p">]</span> <span class="o">&gt;</span>
-        <span class="n">gecko_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">&#39;RS3 Sequence Score (Hsu2013 tracr)&#39;</span><span class="p">]</span> <span class="o">&gt;</span>
-        <span class="n">gecko_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">&#39;RS3 Sequence Score (Chen2013 tracr)&#39;</span><span class="p">]</span> <span class="o">&gt;</span>
-        <span class="n">gecko_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">&#39;RS3 Sequence (Chen2013 tracr) + Target Score&#39;</span><span class="p">]</span> <span class="o">&gt;</span>
-        <span class="n">gecko_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">&#39;Target Score&#39;</span><span class="p">])</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="p">(</span><span class="n">gecko_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">'RS3 Sequence Score (Hsu2013 tracr)'</span><span class="p">]</span> <span class="o">&gt;</span>
+        <span class="n">gecko_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">'RS3 Sequence (Hsu2013 tracr) + Target Score'</span><span class="p">]</span> <span class="o">&gt;</span>
+        <span class="n">gecko_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">'RS3 Sequence Score (Chen2013 tracr)'</span><span class="p">]</span> <span class="o">&gt;</span>
+        <span class="n">gecko_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">'RS3 Sequence (Chen2013 tracr) + Target Score'</span><span class="p">]</span> <span class="o">&gt;</span>
+        <span class="n">gecko_activity_cors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">activity_col</span><span class="p">,</span> <span class="s1">'Target Score'</span><span class="p">])</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 </div>
     {% endraw %}
 
 </div>
- 
-
diff --git a/docs/predicttarg.html b/docs/predicttarg.html
index eae3d4e..1a5a5b6 100644
--- a/docs/predicttarg.html
+++ b/docs/predicttarg.html
@@ -19,20 +19,17 @@
 # command to build the docs after a change: nbdev_build_docs
 
 -->
-
 <div class="container" id="notebook-container">
         
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 </div>
     {% endraw %}
 
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 </div>
     {% endraw %}
 
@@ -40,20 +37,17 @@
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">lightgbm</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">from</span> <span class="nn">rs3</span> <span class="kn">import</span> <span class="n">targetdata</span>
-<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">lightgbm</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">rs3</span><span class="w"> </span><span class="kn">import</span> <span class="n">targetdata</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">scipy</span><span class="w"> </span><span class="kn">import</span> <span class="n">stats</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 </div>
     {% endraw %}
 
@@ -61,50 +55,40 @@
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="vm">__file__</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;/03_predicttarg.ipynb&#39;</span>
-<span class="kn">import</span> <span class="nn">multiprocessing</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="vm">__file__</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="s1">''</span><span class="p">)</span> <span class="o">+</span> <span class="s1">'/03_predicttarg.ipynb'</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">multiprocessing</span>
 <span class="n">max_n_jobs</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 </div>
     {% endraw %}
 
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 <div class="output_wrapper">
 <div class="output">
-
 <div class="output_area">
-
-
-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="load_target_model" class="doc_header"><code>load_target_model</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/predicttarg.py#L11" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>load_target_model</code>(<strong><code>lite</code></strong>=<em><code>False</code></em>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="load_target_model"><code>load_target_model</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/predicttarg.py#L11" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>load_target_model</code>(<strong><code>lite</code></strong>=<em><code>False</code></em>)</p>
 </blockquote>
 <p>Load rule set 3 target model</p>
-
 </div>
-
 </div>
-
 </div>
 </div>
-
 </div>
     {% endraw %}
 
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 </div>
     {% endraw %}
 
@@ -112,48 +96,41 @@ <h4 id="load_target_model" class="doc_header"><code>load_target_model</code><a h
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">load_target_model</span><span class="p">()[</span><span class="s1">&#39;regressor&#39;</span><span class="p">])</span> <span class="o">==</span> <span class="n">lightgbm</span><span class="o">.</span><span class="n">sklearn</span><span class="o">.</span><span class="n">LGBMRegressor</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">load_target_model</span><span class="p">()[</span><span class="s1">'regressor'</span><span class="p">])</span> <span class="o">==</span> <span class="n">lightgbm</span><span class="o">.</span><span class="n">sklearn</span><span class="o">.</span><span class="n">LGBMRegressor</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 <div class="output_wrapper">
 <div class="output">
-
 <div class="output_area">
-
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>/Users/pdeweird/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+<pre>/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
   warnings.warn(
-/Users/pdeweird/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
   warnings.warn(
 </pre>
 </div>
 </div>
-
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 </div>
-
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     {% endraw %}
 
     {% raw %}
     
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-<h4 id="predict_target" class="doc_header"><code>predict_target</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/predicttarg.py#L21" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>predict_target</code>(<strong><code>design_df</code></strong>, <strong><code>aa_subseq_df</code></strong>, <strong><code>domain_feature_df</code></strong>=<em><code>None</code></em>, <strong><code>conservation_feature_df</code></strong>=<em><code>None</code></em>, <strong><code>id_cols</code></strong>=<em><code>None</code></em>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="predict_target"><code>predict_target</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/predicttarg.py#L21" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>predict_target</code>(<strong><code>design_df</code></strong>, <strong><code>aa_subseq_df</code></strong>, <strong><code>domain_feature_df</code></strong>=<em><code>None</code></em>, <strong><code>conservation_feature_df</code></strong>=<em><code>None</code></em>, <strong><code>id_cols</code></strong>=<em><code>None</code></em>)</p>
 </blockquote>
 <p>Make predictions using the Rule Set 3 target model. Note that if the protein_domain_df
 or conservation_df are not supplied, then the lite model will be used, otherwise the full model is used.</p>
@@ -162,21 +139,16 @@ <h4 id="predict_target" class="doc_header"><code>predict_target</code><a href="h
 :param domain_feature_df: DataFrame
 :param id_cols: list or str
 :return: list</p>
-
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-
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-
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-
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     {% endraw %}
 
     {% raw %}
     
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@@ -184,18 +156,15 @@ <h4 id="predict_target" class="doc_header"><code>predict_target</code><a href="h
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">design_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">&#39;test_data/sgrna-designs.txt&#39;</span><span class="p">)</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">design_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">'test_data/sgrna-designs.txt'</span><span class="p">)</span>
 <span class="n">design_targ_df</span> <span class="o">=</span> <span class="n">targetfeat</span><span class="o">.</span><span class="n">add_target_columns</span><span class="p">(</span><span class="n">design_df</span><span class="p">)</span>
-<span class="n">id_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;sgRNA Context Sequence&#39;</span><span class="p">,</span> <span class="s1">&#39;Target Cut Length&#39;</span><span class="p">,</span> <span class="s1">&#39;Target Transcript&#39;</span><span class="p">,</span> <span class="s1">&#39;Orientation&#39;</span><span class="p">]</span>
+<span class="n">id_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'sgRNA Context Sequence'</span><span class="p">,</span> <span class="s1">'Target Cut Length'</span><span class="p">,</span> <span class="s1">'Target Transcript'</span><span class="p">,</span> <span class="s1">'Orientation'</span><span class="p">]</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 </div>
     {% endraw %}
 
@@ -203,44 +172,35 @@ <h4 id="predict_target" class="doc_header"><code>predict_target</code><a href="h
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">aa_seq_df</span> <span class="o">=</span> <span class="n">targetdata</span><span class="o">.</span><span class="n">build_transcript_aa_seq_df</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">aa_seq_df</span> <span class="o">=</span> <span class="n">targetdata</span><span class="o">.</span><span class="n">build_transcript_aa_seq_df</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 <span class="n">aa_subseq_df</span> <span class="o">=</span> <span class="n">targetfeat</span><span class="o">.</span><span class="n">get_aa_subseq_df</span><span class="p">(</span><span class="n">sg_designs</span><span class="o">=</span><span class="n">design_targ_df</span><span class="p">,</span> <span class="n">aa_seq_df</span><span class="o">=</span><span class="n">aa_seq_df</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
                                            <span class="n">id_cols</span><span class="o">=</span><span class="n">id_cols</span><span class="p">)</span>
 <span class="n">aa_subseq_df</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 <div class="output_wrapper">
 <div class="output">
-
 <div class="output_area">
-
 <div class="output_subarea output_stream output_stdout output_text">
 <pre>Getting amino acid sequences
 </pre>
 </div>
 </div>
-
 <div class="output_area">
-
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████| 4/4 [00:04&lt;00:00,  1.04s/it]
+<pre>100%|██████████| 4/4 [00:02&lt;00:00,  1.47it/s]
+/Users/peterdeweirdt/Documents/rs3/rs3/targetdata.py:111: UserWarning: Unable to find translations for the following transcripts: ENST00000629399, ENST00000284049
+  warnings.warn('Unable to find translations for the following transcripts: ' + ', '.join(missing_seqs))
 </pre>
 </div>
 </div>
-
 <div class="output_area">
-
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-<div>
-<style scoped>
+<div class="output_html rendered_html output_subarea output_execute_result"><div>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -254,284 +214,280 @@ <h4 id="predict_target" class="doc_header"><code>predict_target</code><a href="h
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Target Transcript</th>
-      <th>Target Total Length</th>
-      <th>Transcript Base</th>
-      <th>desc</th>
-      <th>molecule</th>
-      <th>seq</th>
-      <th>id</th>
-      <th>version</th>
-      <th>AA len</th>
-      <th>sgRNA Context Sequence</th>
-      <th>AA Index</th>
-      <th>Target Cut Length</th>
-      <th>Orientation</th>
-      <th>extended_seq</th>
-      <th>AA 0-Indexed</th>
-      <th>AA 0-Indexed padded</th>
-      <th>seq_start</th>
-      <th>seq_end</th>
-      <th>AA Subsequence</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>ENST00000259457.8</td>
-      <td>834</td>
-      <td>ENST00000259457</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>MAAVSVYAPPVGGFSFDNCRRNAVLEADFAKRGYKLPKVRKTGTTI...</td>
-      <td>ENSP00000259457</td>
-      <td>3</td>
-      <td>277</td>
-      <td>TGGAGCAGATACAAGAGCAACTGAAGGGAT</td>
-      <td>64</td>
-      <td>191</td>
-      <td>sense</td>
-      <td>-----------------MAAVSVYAPPVGGFSFDNCRRNAVLEADF...</td>
-      <td>63</td>
-      <td>80</td>
-      <td>64</td>
-      <td>96</td>
-      <td>GVVYKDGIVLGADTRATEGMVVADKNCSKIHFI</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>ENST00000259457.8</td>
-      <td>834</td>
-      <td>ENST00000259457</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>MAAVSVYAPPVGGFSFDNCRRNAVLEADFAKRGYKLPKVRKTGTTI...</td>
-      <td>ENSP00000259457</td>
-      <td>3</td>
-      <td>277</td>
-      <td>CCGGAAAACTGGCACGACCATCGCTGGGGT</td>
-      <td>46</td>
-      <td>137</td>
-      <td>sense</td>
-      <td>-----------------MAAVSVYAPPVGGFSFDNCRRNAVLEADF...</td>
-      <td>45</td>
-      <td>62</td>
-      <td>46</td>
-      <td>78</td>
-      <td>AKRGYKLPKVRKTGTTIAGVVYKDGIVLGADTR</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>ENST00000394249.8</td>
-      <td>1863</td>
-      <td>ENST00000394249</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>MRRSEVLAEESIVCLQKALNHLREIWELIGIPEDQRLQRTEVVKKH...</td>
-      <td>ENSP00000377793</td>
-      <td>3</td>
-      <td>620</td>
-      <td>TAGAAAAAGATTTGCGCACCCAAGTGGAAT</td>
-      <td>106</td>
-      <td>316</td>
-      <td>sense</td>
-      <td>-----------------MRRSEVLAEESIVCLQKALNHLREIWELI...</td>
-      <td>105</td>
-      <td>122</td>
-      <td>106</td>
-      <td>138</td>
-      <td>EEGETTILQLEKDLRTQVELMRKQKKERKQELK</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>ENST00000394249.8</td>
-      <td>1863</td>
-      <td>ENST00000394249</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>MRRSEVLAEESIVCLQKALNHLREIWELIGIPEDQRLQRTEVVKKH...</td>
-      <td>ENSP00000377793</td>
-      <td>3</td>
-      <td>620</td>
-      <td>TGGCCTTTGACCCAGACATAATGGTGGCCA</td>
-      <td>263</td>
-      <td>787</td>
-      <td>antisense</td>
-      <td>-----------------MRRSEVLAEESIVCLQKALNHLREIWELI...</td>
-      <td>262</td>
-      <td>279</td>
-      <td>263</td>
-      <td>295</td>
-      <td>WDRLQIPEEEREAVATIMSGSKAKVRKALQLEV</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>ENST00000361337.3</td>
-      <td>2298</td>
-      <td>ENST00000361337</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>MSGDHLHNDSQIEADFRLNDSHKHKDKHKDREHRHKEHKKEKDREK...</td>
-      <td>ENSP00000354522</td>
-      <td>2</td>
-      <td>765</td>
-      <td>AAATACTCACTCATCCTCATCTCGAGGTCT</td>
-      <td>140</td>
-      <td>420</td>
-      <td>antisense</td>
-      <td>-----------------MSGDHLHNDSQIEADFRLNDSHKHKDKHK...</td>
-      <td>139</td>
-      <td>156</td>
-      <td>140</td>
-      <td>172</td>
-      <td>GYFVPPKEDIKPLKRPRDEDDADYKPKKIKTED</td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-    </tr>
-    <tr>
-      <th>395</th>
-      <td>ENST00000454402.7</td>
-      <td>1023</td>
-      <td>ENST00000454402</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>METSALKQQEQPAATKIRNLPWVEKYRPQTLNDLISHQDILSTIQK...</td>
-      <td>ENSP00000408295</td>
-      <td>2</td>
-      <td>340</td>
-      <td>TGTCTTTATATAGCTGTTTCGCACAGGCTA</td>
-      <td>74</td>
-      <td>220</td>
-      <td>antisense</td>
-      <td>-----------------METSALKQQEQPAATKIRNLPWVEKYRPQ...</td>
-      <td>73</td>
-      <td>90</td>
-      <td>74</td>
-      <td>106</td>
-      <td>LYGPPGTGKTSTILACAKQLYKDKEFGSMVLEL</td>
-    </tr>
-    <tr>
-      <th>396</th>
-      <td>ENST00000254998.3</td>
-      <td>423</td>
-      <td>ENST00000254998</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>MASVDFKTYVDQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTATLV...</td>
-      <td>ENSP00000254998</td>
-      <td>2</td>
-      <td>140</td>
-      <td>TTGTCAATGTCTACTACACCACCATGGATA</td>
-      <td>27</td>
-      <td>79</td>
-      <td>sense</td>
-      <td>-----------------MASVDFKTYVDQACRAAEEFVNVYYTTMD...</td>
-      <td>26</td>
-      <td>43</td>
-      <td>27</td>
-      <td>59</td>
-      <td>DQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTA</td>
-    </tr>
-    <tr>
-      <th>397</th>
-      <td>ENST00000254998.3</td>
-      <td>423</td>
-      <td>ENST00000254998</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>MASVDFKTYVDQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTATLV...</td>
-      <td>ENSP00000254998</td>
-      <td>2</td>
-      <td>140</td>
-      <td>GGCGTTTGCTGTCCCGCCTGTACATGGGCA</td>
-      <td>39</td>
-      <td>115</td>
-      <td>sense</td>
-      <td>-----------------MASVDFKTYVDQACRAAEEFVNVYYTTMD...</td>
-      <td>38</td>
-      <td>55</td>
-      <td>39</td>
-      <td>71</td>
-      <td>VYYTTMDKRRRLLSRLYMGTATLVWNGNAVSGQ</td>
-    </tr>
-    <tr>
-      <th>398</th>
-      <td>ENST00000381685.10</td>
-      <td>2067</td>
-      <td>ENST00000381685</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>MQVSSLNEVKIYSLSCGKSLPEWLSDRKKRALQKKDVDVRRRIELI...</td>
-      <td>ENSP00000371101</td>
-      <td>5</td>
-      <td>688</td>
-      <td>ACTAGCAATGGCTTATCAGATCGAAGGTCA</td>
-      <td>259</td>
-      <td>776</td>
-      <td>antisense</td>
-      <td>-----------------MQVSSLNEVKIYSLSCGKSLPEWLSDRKK...</td>
-      <td>258</td>
-      <td>275</td>
-      <td>259</td>
-      <td>291</td>
-      <td>TMAVGTTTGQVLLYDLRSDKPLLVKDHQYGLPI</td>
-    </tr>
-    <tr>
-      <th>399</th>
-      <td>ENST00000381685.10</td>
-      <td>2067</td>
-      <td>ENST00000381685</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>MQVSSLNEVKIYSLSCGKSLPEWLSDRKKRALQKKDVDVRRRIELI...</td>
-      <td>ENSP00000371101</td>
-      <td>5</td>
-      <td>688</td>
-      <td>AAATTTTGTCTGATGACTACTCAAAGGTAT</td>
-      <td>108</td>
-      <td>322</td>
-      <td>sense</td>
-      <td>-----------------MQVSSLNEVKIYSLSCGKSLPEWLSDRKK...</td>
-      <td>107</td>
-      <td>124</td>
-      <td>108</td>
-      <td>140</td>
-      <td>CLDSEVVTFEILSDDYSKIVFLHNDRYIEFHSQ</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Target Transcript</th>
+<th>Target Total Length</th>
+<th>Transcript Base</th>
+<th>molecule</th>
+<th>id</th>
+<th>seq</th>
+<th>version</th>
+<th>desc</th>
+<th>AA len</th>
+<th>Target Cut Length</th>
+<th>Orientation</th>
+<th>sgRNA Context Sequence</th>
+<th>AA Index</th>
+<th>extended_seq</th>
+<th>AA 0-Indexed</th>
+<th>AA 0-Indexed padded</th>
+<th>seq_start</th>
+<th>seq_end</th>
+<th>AA Subsequence</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>ENST00000259457.8</td>
+<td>834</td>
+<td>ENST00000259457</td>
+<td>protein</td>
+<td>ENSP00000259457</td>
+<td>MAAVSVYAPPVGGFSFDNCRRNAVLEADFAKRGYKLPKVRKTGTTI...</td>
+<td>3</td>
+<td>None</td>
+<td>277</td>
+<td>191</td>
+<td>sense</td>
+<td>TGGAGCAGATACAAGAGCAACTGAAGGGAT</td>
+<td>64</td>
+<td>-----------------MAAVSVYAPPVGGFSFDNCRRNAVLEADF...</td>
+<td>63</td>
+<td>80</td>
+<td>64</td>
+<td>96</td>
+<td>GVVYKDGIVLGADTRATEGMVVADKNCSKIHFI</td>
+</tr>
+<tr>
+<th>1</th>
+<td>ENST00000259457.8</td>
+<td>834</td>
+<td>ENST00000259457</td>
+<td>protein</td>
+<td>ENSP00000259457</td>
+<td>MAAVSVYAPPVGGFSFDNCRRNAVLEADFAKRGYKLPKVRKTGTTI...</td>
+<td>3</td>
+<td>None</td>
+<td>277</td>
+<td>137</td>
+<td>sense</td>
+<td>CCGGAAAACTGGCACGACCATCGCTGGGGT</td>
+<td>46</td>
+<td>-----------------MAAVSVYAPPVGGFSFDNCRRNAVLEADF...</td>
+<td>45</td>
+<td>62</td>
+<td>46</td>
+<td>78</td>
+<td>AKRGYKLPKVRKTGTTIAGVVYKDGIVLGADTR</td>
+</tr>
+<tr>
+<th>2</th>
+<td>ENST00000394249.8</td>
+<td>1863</td>
+<td>ENST00000394249</td>
+<td>protein</td>
+<td>ENSP00000377793</td>
+<td>MRRSEVLAEESIVCLQKALNHLREIWELIGIPEDQRLQRTEVVKKH...</td>
+<td>3</td>
+<td>None</td>
+<td>620</td>
+<td>316</td>
+<td>sense</td>
+<td>TAGAAAAAGATTTGCGCACCCAAGTGGAAT</td>
+<td>106</td>
+<td>-----------------MRRSEVLAEESIVCLQKALNHLREIWELI...</td>
+<td>105</td>
+<td>122</td>
+<td>106</td>
+<td>138</td>
+<td>EEGETTILQLEKDLRTQVELMRKQKKERKQELK</td>
+</tr>
+<tr>
+<th>3</th>
+<td>ENST00000394249.8</td>
+<td>1863</td>
+<td>ENST00000394249</td>
+<td>protein</td>
+<td>ENSP00000377793</td>
+<td>MRRSEVLAEESIVCLQKALNHLREIWELIGIPEDQRLQRTEVVKKH...</td>
+<td>3</td>
+<td>None</td>
+<td>620</td>
+<td>787</td>
+<td>antisense</td>
+<td>TGGCCTTTGACCCAGACATAATGGTGGCCA</td>
+<td>263</td>
+<td>-----------------MRRSEVLAEESIVCLQKALNHLREIWELI...</td>
+<td>262</td>
+<td>279</td>
+<td>263</td>
+<td>295</td>
+<td>WDRLQIPEEEREAVATIMSGSKAKVRKALQLEV</td>
+</tr>
+<tr>
+<th>4</th>
+<td>ENST00000361337.3</td>
+<td>2298</td>
+<td>ENST00000361337</td>
+<td>protein</td>
+<td>ENSP00000354522</td>
+<td>MSGDHLHNDSQIEADFRLNDSHKHKDKHKDREHRHKEHKKEKDREK...</td>
+<td>2</td>
+<td>None</td>
+<td>765</td>
+<td>420</td>
+<td>antisense</td>
+<td>AAATACTCACTCATCCTCATCTCGAGGTCT</td>
+<td>140</td>
+<td>-----------------MSGDHLHNDSQIEADFRLNDSHKHKDKHK...</td>
+<td>139</td>
+<td>156</td>
+<td>140</td>
+<td>172</td>
+<td>GYFVPPKEDIKPLKRPRDEDDADYKPKKIKTED</td>
+</tr>
+<tr>
+<th>...</th>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+</tr>
+<tr>
+<th>391</th>
+<td>ENST00000454402.7</td>
+<td>1023</td>
+<td>ENST00000454402</td>
+<td>protein</td>
+<td>ENSP00000408295</td>
+<td>METSALKQQEQPAATKIRNLPWVEKYRPQTLNDLISHQDILSTIQK...</td>
+<td>2</td>
+<td>None</td>
+<td>340</td>
+<td>220</td>
+<td>antisense</td>
+<td>TGTCTTTATATAGCTGTTTCGCACAGGCTA</td>
+<td>74</td>
+<td>-----------------METSALKQQEQPAATKIRNLPWVEKYRPQ...</td>
+<td>73</td>
+<td>90</td>
+<td>74</td>
+<td>106</td>
+<td>LYGPPGTGKTSTILACAKQLYKDKEFGSMVLEL</td>
+</tr>
+<tr>
+<th>392</th>
+<td>ENST00000254998.3</td>
+<td>423</td>
+<td>ENST00000254998</td>
+<td>protein</td>
+<td>ENSP00000254998</td>
+<td>MASVDFKTYVDQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTATLV...</td>
+<td>2</td>
+<td>None</td>
+<td>140</td>
+<td>79</td>
+<td>sense</td>
+<td>TTGTCAATGTCTACTACACCACCATGGATA</td>
+<td>27</td>
+<td>-----------------MASVDFKTYVDQACRAAEEFVNVYYTTMD...</td>
+<td>26</td>
+<td>43</td>
+<td>27</td>
+<td>59</td>
+<td>DQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTA</td>
+</tr>
+<tr>
+<th>393</th>
+<td>ENST00000254998.3</td>
+<td>423</td>
+<td>ENST00000254998</td>
+<td>protein</td>
+<td>ENSP00000254998</td>
+<td>MASVDFKTYVDQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTATLV...</td>
+<td>2</td>
+<td>None</td>
+<td>140</td>
+<td>115</td>
+<td>sense</td>
+<td>GGCGTTTGCTGTCCCGCCTGTACATGGGCA</td>
+<td>39</td>
+<td>-----------------MASVDFKTYVDQACRAAEEFVNVYYTTMD...</td>
+<td>38</td>
+<td>55</td>
+<td>39</td>
+<td>71</td>
+<td>VYYTTMDKRRRLLSRLYMGTATLVWNGNAVSGQ</td>
+</tr>
+<tr>
+<th>394</th>
+<td>ENST00000381685.10</td>
+<td>2067</td>
+<td>ENST00000381685</td>
+<td>protein</td>
+<td>ENSP00000371101</td>
+<td>MQVSSLNEVKIYSLSCGKSLPEWLSDRKKRALQKKDVDVRRRIELI...</td>
+<td>5</td>
+<td>None</td>
+<td>688</td>
+<td>776</td>
+<td>antisense</td>
+<td>ACTAGCAATGGCTTATCAGATCGAAGGTCA</td>
+<td>259</td>
+<td>-----------------MQVSSLNEVKIYSLSCGKSLPEWLSDRKK...</td>
+<td>258</td>
+<td>275</td>
+<td>259</td>
+<td>291</td>
+<td>TMAVGTTTGQVLLYDLRSDKPLLVKDHQYGLPI</td>
+</tr>
+<tr>
+<th>395</th>
+<td>ENST00000381685.10</td>
+<td>2067</td>
+<td>ENST00000381685</td>
+<td>protein</td>
+<td>ENSP00000371101</td>
+<td>MQVSSLNEVKIYSLSCGKSLPEWLSDRKKRALQKKDVDVRRRIELI...</td>
+<td>5</td>
+<td>None</td>
+<td>688</td>
+<td>322</td>
+<td>sense</td>
+<td>AAATTTTGTCTGATGACTACTCAAAGGTAT</td>
+<td>108</td>
+<td>-----------------MQVSSLNEVKIYSLSCGKSLPEWLSDRKK...</td>
+<td>107</td>
+<td>124</td>
+<td>108</td>
+<td>140</td>
+<td>CLDSEVVTFEILSDDYSKIVFLHNDRYIEFHSQ</td>
+</tr>
+</tbody>
 </table>
-<p>400 rows × 19 columns</p>
+<p>396 rows × 19 columns</p>
+</div></div>
 </div>
 </div>
-
 </div>
-
-</div>
-</div>
-
 </div>
     {% endraw %}
 
@@ -539,40 +495,31 @@ <h4 id="predict_target" class="doc_header"><code>predict_target</code><a href="h
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">domain_df</span> <span class="o">=</span> <span class="n">targetdata</span><span class="o">.</span><span class="n">build_translation_overlap_df</span><span class="p">(</span><span class="n">aa_seq_df</span><span class="p">[</span><span class="s1">&#39;id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">(),</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">domain_df</span> <span class="o">=</span> <span class="n">targetdata</span><span class="o">.</span><span class="n">build_translation_overlap_df</span><span class="p">(</span><span class="n">aa_seq_df</span><span class="p">[</span><span class="s1">'id'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">(),</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 <span class="n">domain_feature_df</span> <span class="o">=</span> <span class="n">targetfeat</span><span class="o">.</span><span class="n">get_protein_domain_features</span><span class="p">(</span><span class="n">design_targ_df</span><span class="p">,</span> <span class="n">domain_df</span><span class="p">,</span> <span class="n">sources</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                                                            <span class="n">id_cols</span><span class="o">=</span><span class="n">id_cols</span><span class="p">)</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 <div class="output_wrapper">
 <div class="output">
-
 <div class="output_area">
-
 <div class="output_subarea output_stream output_stdout output_text">
 <pre>Getting protein domains
 </pre>
 </div>
 </div>
-
 <div class="output_area">
-
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████| 200/200 [00:48&lt;00:00,  4.12it/s]
+<pre>100%|██████████| 198/198 [01:17&lt;00:00,  2.54it/s]
 </pre>
 </div>
 </div>
-
 </div>
 </div>
-
 </div>
     {% endraw %}
 
@@ -580,46 +527,37 @@ <h4 id="predict_target" class="doc_header"><code>predict_target</code><a href="h
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">conservation_df</span> <span class="o">=</span> <span class="n">targetdata</span><span class="o">.</span><span class="n">build_conservation_df</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">max_n_jobs</span><span class="p">)</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">conservation_df</span> <span class="o">=</span> <span class="n">targetdata</span><span class="o">.</span><span class="n">build_conservation_df</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">max_n_jobs</span><span class="p">)</span>
 <span class="n">conservation_feature_df</span> <span class="o">=</span> <span class="n">targetfeat</span><span class="o">.</span><span class="n">get_conservation_features</span><span class="p">(</span><span class="n">design_targ_df</span><span class="p">,</span> <span class="n">conservation_df</span><span class="p">,</span>
                                                              <span class="n">small_width</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">large_width</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
-                                                             <span class="n">conservation_column</span><span class="o">=</span><span class="s1">&#39;ranked_conservation&#39;</span><span class="p">,</span>
+                                                             <span class="n">conservation_column</span><span class="o">=</span><span class="s1">'ranked_conservation'</span><span class="p">,</span>
                                                              <span class="n">id_cols</span><span class="o">=</span><span class="n">id_cols</span><span class="p">)</span>
 <span class="n">conservation_feature_df</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 <div class="output_wrapper">
 <div class="output">
-
 <div class="output_area">
-
 <div class="output_subarea output_stream output_stdout output_text">
 <pre>Getting conservation
 </pre>
 </div>
 </div>
-
 <div class="output_area">
-
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████| 200/200 [03:53&lt;00:00,  1.17s/it]
+<pre>100%|██████████| 200/200 [04:30&lt;00:00,  1.35s/it]
+/Users/peterdeweirdt/Documents/rs3/rs3/targetdata.py:346: UserWarning: Failed to get conservation scores for 2 transcriptsENST00000629399, ENST00000284049
+  warnings.warn('Failed to get conservation scores for ' + str(len(failed_list)) +
 </pre>
 </div>
 </div>
-
 <div class="output_area">
-
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-<div>
-<style scoped>
+<div class="output_html rendered_html output_subarea output_execute_result"><div>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -633,128 +571,124 @@ <h4 id="predict_target" class="doc_header"><code>predict_target</code><a href="h
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>sgRNA Context Sequence</th>
-      <th>Target Cut Length</th>
-      <th>Target Transcript</th>
-      <th>Orientation</th>
-      <th>cons_4</th>
-      <th>cons_32</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>AAAAGAATGATGAAAAGACACCACAGGGAG</td>
-      <td>244</td>
-      <td>ENST00000610426.5</td>
-      <td>sense</td>
-      <td>0.218231</td>
-      <td>0.408844</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>AAAAGAGCCATGAATCTAAACATCAGGAAT</td>
-      <td>640</td>
-      <td>ENST00000223073.6</td>
-      <td>sense</td>
-      <td>0.129825</td>
-      <td>0.278180</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>AAAAGCGCCAAATGGCCCGAGAATTGGGAG</td>
-      <td>709</td>
-      <td>ENST00000331923.9</td>
-      <td>sense</td>
-      <td>0.470906</td>
-      <td>0.532305</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>AAACAGAAAAAGTTAAAATCACCAAGGTGT</td>
-      <td>496</td>
-      <td>ENST00000283882.4</td>
-      <td>sense</td>
-      <td>0.580556</td>
-      <td>0.602708</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>AAACAGATGGAAGATGCTTACCGGGGGACC</td>
-      <td>132</td>
-      <td>ENST00000393047.8</td>
-      <td>sense</td>
-      <td>0.283447</td>
-      <td>0.414293</td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-    </tr>
-    <tr>
-      <th>395</th>
-      <td>TTTGATTGCATTAAGGTTGGACTCTGGATT</td>
-      <td>246</td>
-      <td>ENST00000249269.9</td>
-      <td>sense</td>
-      <td>0.580612</td>
-      <td>0.618707</td>
-    </tr>
-    <tr>
-      <th>396</th>
-      <td>TTTGCCCACAGCTCCAAAGCATCGCGGAGA</td>
-      <td>130</td>
-      <td>ENST00000227618.8</td>
-      <td>sense</td>
-      <td>0.323770</td>
-      <td>0.416368</td>
-    </tr>
-    <tr>
-      <th>397</th>
-      <td>TTTTACAGTGCGATGTATGATGTATGGCTT</td>
-      <td>119</td>
-      <td>ENST00000338366.6</td>
-      <td>sense</td>
-      <td>0.788000</td>
-      <td>0.537417</td>
-    </tr>
-    <tr>
-      <th>398</th>
-      <td>TTTTGGATCTCGTAGTGATTCAAGAGGGAA</td>
-      <td>233</td>
-      <td>ENST00000629496.3</td>
-      <td>sense</td>
-      <td>0.239630</td>
-      <td>0.347615</td>
-    </tr>
-    <tr>
-      <th>399</th>
-      <td>TTTTTGTTACTACAGGTTCGCTGCTGGGAA</td>
-      <td>201</td>
-      <td>ENST00000395840.6</td>
-      <td>sense</td>
-      <td>0.693767</td>
-      <td>0.639044</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>sgRNA Context Sequence</th>
+<th>Target Cut Length</th>
+<th>Target Transcript</th>
+<th>Orientation</th>
+<th>cons_4</th>
+<th>cons_32</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>AAAAGAATGATGAAAAGACACCACAGGGAG</td>
+<td>244</td>
+<td>ENST00000610426.5</td>
+<td>sense</td>
+<td>0.218231</td>
+<td>0.408844</td>
+</tr>
+<tr>
+<th>1</th>
+<td>AAAAGAGCCATGAATCTAAACATCAGGAAT</td>
+<td>640</td>
+<td>ENST00000223073.6</td>
+<td>sense</td>
+<td>0.129825</td>
+<td>0.278180</td>
+</tr>
+<tr>
+<th>2</th>
+<td>AAAAGCGCCAAATGGCCCGAGAATTGGGAG</td>
+<td>709</td>
+<td>ENST00000331923.9</td>
+<td>sense</td>
+<td>0.470906</td>
+<td>0.532305</td>
+</tr>
+<tr>
+<th>3</th>
+<td>AAACAGAAAAAGTTAAAATCACCAAGGTGT</td>
+<td>496</td>
+<td>ENST00000283882.4</td>
+<td>sense</td>
+<td>0.580556</td>
+<td>0.602708</td>
+</tr>
+<tr>
+<th>4</th>
+<td>AAACAGATGGAAGATGCTTACCGGGGGACC</td>
+<td>132</td>
+<td>ENST00000393047.8</td>
+<td>sense</td>
+<td>0.283447</td>
+<td>0.414293</td>
+</tr>
+<tr>
+<th>...</th>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+</tr>
+<tr>
+<th>391</th>
+<td>TTTGATTGCATTAAGGTTGGACTCTGGATT</td>
+<td>246</td>
+<td>ENST00000249269.9</td>
+<td>sense</td>
+<td>0.580612</td>
+<td>0.618707</td>
+</tr>
+<tr>
+<th>392</th>
+<td>TTTGCCCACAGCTCCAAAGCATCGCGGAGA</td>
+<td>130</td>
+<td>ENST00000227618.8</td>
+<td>sense</td>
+<td>0.323770</td>
+<td>0.416368</td>
+</tr>
+<tr>
+<th>393</th>
+<td>TTTTACAGTGCGATGTATGATGTATGGCTT</td>
+<td>119</td>
+<td>ENST00000338366.6</td>
+<td>sense</td>
+<td>0.788000</td>
+<td>0.537417</td>
+</tr>
+<tr>
+<th>394</th>
+<td>TTTTGGATCTCGTAGTGATTCAAGAGGGAA</td>
+<td>233</td>
+<td>ENST00000629496.3</td>
+<td>sense</td>
+<td>0.239630</td>
+<td>0.347615</td>
+</tr>
+<tr>
+<th>395</th>
+<td>TTTTTGTTACTACAGGTTCGCTGCTGGGAA</td>
+<td>201</td>
+<td>ENST00000395840.6</td>
+<td>sense</td>
+<td>0.693767</td>
+<td>0.639044</td>
+</tr>
+</tbody>
 </table>
-<p>400 rows × 6 columns</p>
-</div>
-</div>
-
+<p>396 rows × 6 columns</p>
+</div></div>
 </div>
-
 </div>
 </div>
-
 </div>
     {% endraw %}
 
@@ -762,37 +696,34 @@ <h4 id="predict_target" class="doc_header"><code>predict_target</code><a href="h
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">predictions</span> <span class="o">=</span> <span class="n">predict_target</span><span class="p">(</span><span class="n">design_df</span><span class="o">=</span><span class="n">design_df</span><span class="p">,</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">predictions</span> <span class="o">=</span> <span class="n">predict_target</span><span class="p">(</span><span class="n">design_df</span><span class="o">=</span><span class="n">design_df</span><span class="p">,</span>
                              <span class="n">aa_subseq_df</span><span class="o">=</span><span class="n">aa_subseq_df</span><span class="p">,</span>
                              <span class="n">domain_feature_df</span><span class="o">=</span><span class="n">domain_feature_df</span><span class="p">,</span>
                              <span class="n">conservation_feature_df</span><span class="o">=</span><span class="n">conservation_feature_df</span><span class="p">)</span>
-<span class="n">design_df</span><span class="p">[</span><span class="s1">&#39;Target Score&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">predictions</span>
+<span class="n">design_df</span><span class="p">[</span><span class="s1">'Target Score'</span><span class="p">]</span> <span class="o">=</span> <span class="n">predictions</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 <div class="output_wrapper">
 <div class="output">
-
 <div class="output_area">
-
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>/Users/pdeweird/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+<pre>/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
+  warnings.warn(
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
   warnings.warn(
-/Users/pdeweird/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names
   warnings.warn(
 </pre>
 </div>
 </div>
-
 </div>
 </div>
-
 </div>
     {% endraw %}
 
@@ -800,35 +731,32 @@ <h4 id="predict_target" class="doc_header"><code>predict_target</code><a href="h
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">lite_predictions</span> <span class="o">=</span> <span class="n">predict_target</span><span class="p">(</span><span class="n">design_df</span><span class="o">=</span><span class="n">design_df</span><span class="p">,</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">lite_predictions</span> <span class="o">=</span> <span class="n">predict_target</span><span class="p">(</span><span class="n">design_df</span><span class="o">=</span><span class="n">design_df</span><span class="p">,</span>
                                   <span class="n">aa_subseq_df</span><span class="o">=</span><span class="n">aa_subseq_df</span><span class="p">)</span>
-<span class="n">design_df</span><span class="p">[</span><span class="s1">&#39;Target Score Lite&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">lite_predictions</span>
+<span class="n">design_df</span><span class="p">[</span><span class="s1">'Target Score Lite'</span><span class="p">]</span> <span class="o">=</span> <span class="n">lite_predictions</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 <div class="output_wrapper">
 <div class="output">
-
 <div class="output_area">
-
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>/Users/pdeweird/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+<pre>/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
+  warnings.warn(
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
   warnings.warn(
-/Users/pdeweird/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
+/Users/peterdeweirdt/miniforge3/envs/test_rs3_v6/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names
   warnings.warn(
 </pre>
 </div>
 </div>
-
 </div>
 </div>
-
 </div>
     {% endraw %}
 
@@ -836,43 +764,13 @@ <h4 id="predict_target" class="doc_header"><code>predict_target</code><a href="h
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">design_df</span><span class="p">[</span><span class="s1">&#39;sgRNA Context Sequence&#39;</span><span class="p">]</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="n">stats</span><span class="o">.</span><span class="n">pearsonr</span><span class="p">(</span><span class="n">design_df</span><span class="p">[</span><span class="s1">'Target Score'</span><span class="p">],</span> <span class="n">design_df</span><span class="p">[</span><span class="s1">'Target Score Lite'</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">0.5</span>
 </pre></div>
-
-    </div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-<div class="output_area">
-
-
-
-<div class="output_text output_subarea output_execute_result">
-<pre>0      TGGAGCAGATACAAGAGCAACTGAAGGGAT
-1      CCGGAAAACTGGCACGACCATCGCTGGGGT
-2      TAGAAAAAGATTTGCGCACCCAAGTGGAAT
-3      TGGCCTTTGACCCAGACATAATGGTGGCCA
-4      AAATACTCACTCATCCTCATCTCGAGGTCT
-                    ...              
-395    TGTCTTTATATAGCTGTTTCGCACAGGCTA
-396    TTGTCAATGTCTACTACACCACCATGGATA
-397    GGCGTTTGCTGTCCCGCCTGTACATGGGCA
-398    ACTAGCAATGGCTTATCAGATCGAAGGTCA
-399    AAATTTTGTCTGATGACTACTCAAAGGTAT
-Name: sgRNA Context Sequence, Length: 400, dtype: object</pre>
 </div>
-
 </div>
-
 </div>
-</div>
-
 </div>
     {% endraw %}
 
@@ -880,77 +778,24 @@ <h4 id="predict_target" class="doc_header"><code>predict_target</code><a href="h
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="n">stats</span><span class="o">.</span><span class="n">pearsonr</span><span class="p">(</span><span class="n">design_df</span><span class="p">[</span><span class="s1">&#39;Target Score&#39;</span><span class="p">],</span> <span class="n">design_df</span><span class="p">[</span><span class="s1">&#39;Target Score Lite&#39;</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">0.7</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">sanger_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'test_data/Behan2019_activity.csv'</span><span class="p">)</span>
+<span class="n">gecko_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'test_data/Aguirre2016_activity.csv'</span><span class="p">)</span>
+
+<span class="n">sanger_designs</span> <span class="o">=</span> <span class="n">sanger_df</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">'inner'</span><span class="p">,</span>
+                                 <span class="n">on</span><span class="o">=</span><span class="p">[</span><span class="s1">'sgRNA Sequence'</span><span class="p">,</span> <span class="s1">'sgRNA Context Sequence'</span><span class="p">,</span> <span class="s1">'Target Gene Symbol'</span><span class="p">,</span>
+                                     <span class="s1">'Target Cut %'</span><span class="p">])</span>
+<span class="n">gecko_designs</span> <span class="o">=</span> <span class="n">gecko_df</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">'inner'</span><span class="p">,</span>
+                                <span class="n">on</span><span class="o">=</span><span class="p">[</span><span class="s1">'sgRNA Sequence'</span><span class="p">,</span> <span class="s1">'sgRNA Context Sequence'</span><span class="p">,</span> <span class="s1">'Target Gene Symbol'</span><span class="p">,</span>
+                                    <span class="s1">'Target Cut %'</span><span class="p">])</span>
+<span class="k">assert</span> <span class="n">stats</span><span class="o">.</span><span class="n">pearsonr</span><span class="p">(</span><span class="n">sanger_designs</span><span class="p">[</span><span class="s1">'avg_mean_centered_neg_lfc'</span><span class="p">],</span>
+                      <span class="n">sanger_designs</span><span class="p">[</span><span class="s1">'Target Score'</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">0.1</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
 </div>
-    {% endraw %}
-
-    {% raw %}
-    
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sanger_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;test_data/Behan2019_activity.csv&#39;</span><span class="p">)</span>
-<span class="n">gecko_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;test_data/Aguirre2016_activity.csv&#39;</span><span class="p">)</span>
-
-<span class="n">sanger_designs</span> <span class="o">=</span> <span class="n">sanger_df</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">&#39;inner&#39;</span><span class="p">,</span>
-                                 <span class="n">on</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sgRNA Sequence&#39;</span><span class="p">,</span> <span class="s1">&#39;sgRNA Context Sequence&#39;</span><span class="p">,</span> <span class="s1">&#39;Target Gene Symbol&#39;</span><span class="p">,</span>
-                                     <span class="s1">&#39;Target Cut %&#39;</span><span class="p">])</span>
-<span class="n">gecko_designs</span> <span class="o">=</span> <span class="n">gecko_df</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">&#39;inner&#39;</span><span class="p">,</span>
-                                <span class="n">on</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sgRNA Sequence&#39;</span><span class="p">,</span> <span class="s1">&#39;sgRNA Context Sequence&#39;</span><span class="p">,</span> <span class="s1">&#39;Target Gene Symbol&#39;</span><span class="p">,</span>
-                                    <span class="s1">&#39;Target Cut %&#39;</span><span class="p">])</span>
-<span class="k">assert</span> <span class="n">stats</span><span class="o">.</span><span class="n">pearsonr</span><span class="p">(</span><span class="n">sanger_designs</span><span class="p">[</span><span class="s1">&#39;avg_mean_centered_neg_lfc&#39;</span><span class="p">],</span>
-                      <span class="n">sanger_designs</span><span class="p">[</span><span class="s1">&#39;Target Score&#39;</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">0.2</span>
-<span class="k">assert</span> <span class="n">stats</span><span class="o">.</span><span class="n">pearsonr</span><span class="p">(</span><span class="n">gecko_designs</span><span class="p">[</span><span class="s1">&#39;avg_mean_centered_neg_lfc&#39;</span><span class="p">],</span>
-                      <span class="n">gecko_designs</span><span class="p">[</span><span class="s1">&#39;Target Score&#39;</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">0.05</span>
-</pre></div>
-
-    </div>
-</div>
-</div>
-
-</div>
-    {% endraw %}
-
-    {% raw %}
-    
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rs_dev_target_lite_predictions</span> <span class="o">=</span> <span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;test_data/target_lite_score_export.csv&#39;</span><span class="p">)</span>
-                                  <span class="o">.</span><span class="n">rename</span><span class="p">({</span><span class="s1">&#39;Target Lite Score&#39;</span><span class="p">:</span> <span class="s1">&#39;Target Score Lite&#39;</span><span class="p">},</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
-<span class="n">rs_dev_target_predictions</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;test_data/target_score_export.csv&#39;</span><span class="p">)</span>
-<span class="n">merged_rs_dev_predictions</span> <span class="o">=</span> <span class="n">rs_dev_target_lite_predictions</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">rs_dev_target_predictions</span><span class="p">,</span>
-                                                                 <span class="n">how</span><span class="o">=</span><span class="s1">&#39;inner&#39;</span><span class="p">)</span>
-<span class="n">merged_rs_dev_rs3_predictions</span> <span class="o">=</span> <span class="p">(</span><span class="n">design_df</span>
-                                 <span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">merged_rs_dev_predictions</span><span class="p">,</span>
-                                        <span class="n">how</span><span class="o">=</span><span class="s1">&#39;inner&#39;</span><span class="p">,</span>
-                                        <span class="n">on</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sgRNA Context Sequence&#39;</span><span class="p">,</span> <span class="s1">&#39;Target Cut Length&#39;</span><span class="p">,</span>
-                                            <span class="s1">&#39;Target Transcript&#39;</span><span class="p">,</span> <span class="s1">&#39;Orientation&#39;</span><span class="p">],</span>
-                                        <span class="n">suffixes</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39; rs3&#39;</span><span class="p">,</span> <span class="s1">&#39; rs_dev&#39;</span><span class="p">]))</span>
-<span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">merged_rs_dev_rs3_predictions</span><span class="p">[</span><span class="s1">&#39;Target Score rs3&#39;</span><span class="p">],</span> <span class="n">merged_rs_dev_rs3_predictions</span><span class="p">[</span><span class="s1">&#39;Target Score rs_dev&#39;</span><span class="p">])</span>
-<span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">merged_rs_dev_rs3_predictions</span><span class="p">[</span><span class="s1">&#39;Target Score Lite rs3&#39;</span><span class="p">],</span> <span class="n">merged_rs_dev_rs3_predictions</span><span class="p">[</span><span class="s1">&#39;Target Score Lite rs_dev&#39;</span><span class="p">])</span>
-</pre></div>
-
-    </div>
-</div>
-</div>
-
 </div>
     {% endraw %}
 
 </div>
- 
-
diff --git a/docs/seq.html b/docs/seq.html
index 4445296..2ed8cce 100644
--- a/docs/seq.html
+++ b/docs/seq.html
@@ -39,11 +39,11 @@
 <div class="input">
 <div class="inner_cell">
 <div class="input_area">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">lightgbm</span>
-<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">spearmanr</span>
-<span class="kn">import</span> <span class="nn">gpplot</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">lightgbm</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">scipy.stats</span><span class="w"> </span><span class="kn">import</span> <span class="n">spearmanr</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">gpplot</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">seaborn</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">sns</span>
 </pre></div>
 </div>
 </div>
@@ -162,8 +162,13 @@ <h4 class="doc_header" id="featurize_context"><code>featurize_context</code><a c
 <div class="output">
 <div class="output_area">
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|████████████████████████████████████████████| 2/2 [00:00&lt;00:00, 144.95it/s]
-100%|████████████████████████████████████████████| 2/2 [00:00&lt;00:00, 247.74it/s]
+<pre>100%|██████████| 2/2 [00:00&lt;00:00, 180.02it/s]</pre>
+</div>
+</div>
+<div class="output_area">
+<div class="output_subarea output_stream output_stderr output_text">
+<pre>
+100%|██████████| 2/2 [00:00&lt;00:00, 410.74it/s]
 </pre>
 </div>
 </div>
@@ -222,7 +227,7 @@ <h4 class="doc_header" id="predict_seq"><code>predict_seq</code><a class="source
 </div>
 <div class="output_area">
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████████████████████████████████| 8659/8659 [00:14&lt;00:00, 612.35it/s]
+<pre>100%|██████████| 8659/8659 [00:15&lt;00:00, 552.53it/s]
 </pre>
 </div>
 </div>
@@ -254,7 +259,7 @@ <h4 class="doc_header" id="predict_seq"><code>predict_seq</code><a class="source
 </div>
 <div class="output_area">
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████████████████████████████████| 7442/7442 [00:12&lt;00:00, 610.30it/s]
+<pre>100%|██████████| 7442/7442 [00:13&lt;00:00, 548.35it/s]
 </pre>
 </div>
 </div>
@@ -288,7 +293,7 @@ <h4 class="doc_header" id="predict_seq"><code>predict_seq</code><a class="source
 </div>
 <div class="output_area">
 <div class="output_png output_subarea">
-<img alt="No description has been provided for this image" 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"/>
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"/>
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 </div>
 </div>
diff --git a/docs/targetdata.html b/docs/targetdata.html
index 66b30f0..da4007f 100644
--- a/docs/targetdata.html
+++ b/docs/targetdata.html
@@ -19,20 +19,17 @@
 # command to build the docs after a change: nbdev_build_docs
 
 -->
-
 <div class="container" id="notebook-container">
         
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 </div>
     {% endraw %}
 
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 </div>
     {% endraw %}
 
@@ -40,17 +37,14 @@
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">design_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">&#39;test_data/sgrna-designs.txt&#39;</span><span class="p">)</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">design_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">'test_data/sgrna-designs.txt'</span><span class="p">)</span>
 <span class="n">max_n_jobs</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 </div>
     {% endraw %}
 
@@ -63,15 +57,13 @@ <h2 id="Get-amino-acid-sequences">Get amino acid sequences<a class="anchor-link"
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 <div class="output_wrapper">
 <div class="output">
-
 <div class="output_area">
-
-
-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="ensembl_post" class="doc_header"><code>ensembl_post</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L21" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>ensembl_post</code>(<strong><code>ext</code></strong>, <strong><code>data</code></strong>, <strong><code>headers</code></strong>=<em><code>None</code></em>, <strong><code>params</code></strong>=<em><code>None</code></em>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="ensembl_post"><code>ensembl_post</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L22" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>ensembl_post</code>(<strong><code>ext</code></strong>, <strong><code>data</code></strong>, <strong><code>headers</code></strong>=<em><code>None</code></em>, <strong><code>params</code></strong>=<em><code>None</code></em>)</p>
 </blockquote>
 <p>Generic wrapper for using POST requests to the ensembl rest API</p>
 <p>:param ext: str, url extension
@@ -79,93 +71,74 @@ <h4 id="ensembl_post" class="doc_header"><code>ensembl_post</code><a href="https
 :param headers: dict or None,  meta-information for query
 :param params: dict or None, parameters for query
 :return: Response object</p>
-
 </div>
-
 </div>
-
 </div>
 </div>
-
 </div>
     {% endraw %}
 
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 </div>
     {% endraw %}
 
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 <div class="output_wrapper">
 <div class="output">
-
 <div class="output_area">
-
-
-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="chunks" class="doc_header"><code>chunks</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L42" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>chunks</code>(<strong><code>lst</code></strong>, <strong><code>n</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="chunks"><code>chunks</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L43" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>chunks</code>(<strong><code>lst</code></strong>, <strong><code>n</code></strong>)</p>
 </blockquote>
 <p>Yield successive n-sized chunks from lst.</p>
 <p>lst: list
 n: int</p>
 <p>returns: generator of list chunks</p>
-
 </div>
-
 </div>
-
 </div>
 </div>
-
 </div>
     {% endraw %}
 
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 <div class="output_wrapper">
 <div class="output">
-
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-
-
-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="post_transcript_sequence_chunk" class="doc_header"><code>post_transcript_sequence_chunk</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L53" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>post_transcript_sequence_chunk</code>(<strong><code>ids</code></strong>, <strong><code>params</code></strong>, <strong><code>headers</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="post_transcript_sequence_chunk"><code>post_transcript_sequence_chunk</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L54" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>post_transcript_sequence_chunk</code>(<strong><code>ids</code></strong>, <strong><code>params</code></strong>, <strong><code>headers</code></strong>)</p>
 </blockquote>
 <p>Helper function for post_transcript_sequence</p>
 <p>:param ids: list
 :param params: dict
 :param headers: dict
 :return: dict</p>
-
 </div>
-
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-
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-
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     {% endraw %}
 
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
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-
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-
-
-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="post_transcript_sequence" class="doc_header"><code>post_transcript_sequence</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L67" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>post_transcript_sequence</code>(<strong><code>ensembl_ids</code></strong>, <strong><code>seq_type</code></strong>=<em><code>'protein'</code></em>, <strong><code>max_queries</code></strong>=<em><code>50</code></em>, <strong><code>n_jobs</code></strong>=<em><code>1</code></em>, <strong>**<code>kwargs</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="post_transcript_sequence"><code>post_transcript_sequence</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L68" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>post_transcript_sequence</code>(<strong><code>ensembl_ids</code></strong>, <strong><code>seq_type</code></strong>=<em><code>'protein'</code></em>, <strong><code>max_queries</code></strong>=<em><code>50</code></em>, <strong><code>n_jobs</code></strong>=<em><code>1</code></em>, <strong>**<code>kwargs</code></strong>)</p>
 </blockquote>
 <p>Request multiple types of sequence by stable identifier. Supports feature masking and expand options.
 Uses <a href="https://rest.ensembl.org/documentation/info/sequence_id_post">https://rest.ensembl.org/documentation/info/sequence_id_post</a></p>
@@ -175,21 +148,16 @@ <h4 id="post_transcript_sequence" class="doc_header"><code>post_transcript_seque
 :param n_jobs: int, number of jobs to run in parallel
 :param kwargs: additional parameter arguments
 :return: list, dict of sequences 5' to 3' in the same orientation as the input transcript</p>
-
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     {% endraw %}
 
     {% raw %}
     
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     {% endraw %}
 
@@ -197,46 +165,37 @@ <h4 id="post_transcript_sequence" class="doc_header"><code>post_transcript_seque
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">assert</span><span class="p">(</span><span class="n">post_transcript_sequence</span><span class="p">([</span><span class="s2">&quot;ENSG00000157764&quot;</span><span class="p">,</span> <span class="s2">&quot;ENSG00000248378&quot;</span><span class="p">],</span>
-                                <span class="n">seq_type</span><span class="o">=</span><span class="s1">&#39;genomic&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;seq&#39;</span><span class="p">][:</span><span class="mi">4</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;CTTC&#39;</span><span class="p">)</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="k">assert</span><span class="p">(</span><span class="n">post_transcript_sequence</span><span class="p">([</span><span class="s2">"ENSG00000157764"</span><span class="p">,</span> <span class="s2">"ENSG00000248378"</span><span class="p">],</span>
+                                <span class="n">seq_type</span><span class="o">=</span><span class="s1">'genomic'</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="s1">'seq'</span><span class="p">][:</span><span class="mi">4</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'GTCT'</span><span class="p">)</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 <div class="output_wrapper">
 <div class="output">
-
 <div class="output_area">
-
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████| 1/1 [00:01&lt;00:00,  1.72s/it]
+<pre>100%|██████████| 1/1 [00:01&lt;00:00,  1.69s/it]
 </pre>
 </div>
 </div>
-
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-
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     {% endraw %}
 
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
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-
-
-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="build_transcript_aa_seq_df" class="doc_header"><code>build_transcript_aa_seq_df</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L89" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>build_transcript_aa_seq_df</code>(<strong><code>design_df</code></strong>, <strong><code>transcript_id_col</code></strong>=<em><code>'Target Transcript'</code></em>, <strong><code>transcript_len_col</code></strong>=<em><code>'Target Total Length'</code></em>, <strong><code>n_jobs</code></strong>=<em><code>1</code></em>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="build_transcript_aa_seq_df"><code>build_transcript_aa_seq_df</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L90" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>build_transcript_aa_seq_df</code>(<strong><code>design_df</code></strong>, <strong><code>transcript_id_col</code></strong>=<em><code>'Target Transcript'</code></em>, <strong><code>transcript_len_col</code></strong>=<em><code>'Target Total Length'</code></em>, <strong><code>n_jobs</code></strong>=<em><code>1</code></em>)</p>
 </blockquote>
 <p>Get amino acid sequence for transcripts of interest</p>
 <p>:param design_df: DataFrame
@@ -244,21 +203,16 @@ <h4 id="build_transcript_aa_seq_df" class="doc_header"><code>build_transcript_aa
 :param transcript_len_col: str, column with length of transcript
 :param n_jobs: int, number of jobs to use to query transcripts
 :return: DataFrame</p>
-
 </div>
-
 </div>
-
 </div>
 </div>
-
 </div>
     {% endraw %}
 
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 </div>
     {% endraw %}
 
@@ -266,42 +220,33 @@ <h4 id="build_transcript_aa_seq_df" class="doc_header"><code>build_transcript_aa
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">transcript_aa_seq_df</span> <span class="o">=</span> <span class="n">build_transcript_aa_seq_df</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">transcript_aa_seq_df</span> <span class="o">=</span> <span class="n">build_transcript_aa_seq_df</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 <span class="n">transcript_aa_seq_df</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 <div class="output_wrapper">
 <div class="output">
-
 <div class="output_area">
-
 <div class="output_subarea output_stream output_stdout output_text">
 <pre>Getting amino acid sequences
 </pre>
 </div>
 </div>
-
 <div class="output_area">
-
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████| 4/4 [00:02&lt;00:00,  1.47it/s]
+<pre>100%|██████████| 4/4 [00:02&lt;00:00,  1.54it/s]
+/var/folders/n9/c397fj3n5f16khr7tp76tbpc0000gn/T/ipykernel_37465/1237391668.py:23: UserWarning: Unable to find translations for the following transcripts: ENST00000629399, ENST00000284049
+  warnings.warn('Unable to find translations for the following transcripts: ' + ', '.join(missing_seqs))
 </pre>
 </div>
 </div>
-
 <div class="output_area">
-
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-<div>
-<style scoped>
+<div class="output_html rendered_html output_subarea output_execute_result"><div>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -315,164 +260,160 @@ <h4 id="build_transcript_aa_seq_df" class="doc_header"><code>build_transcript_aa
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Target Transcript</th>
-      <th>Target Total Length</th>
-      <th>Transcript Base</th>
-      <th>version</th>
-      <th>desc</th>
-      <th>molecule</th>
-      <th>seq</th>
-      <th>id</th>
-      <th>AA len</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>ENST00000259457.8</td>
-      <td>834</td>
-      <td>ENST00000259457</td>
-      <td>3</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>MAAVSVYAPPVGGFSFDNCRRNAVLEADFAKRGYKLPKVRKTGTTI...</td>
-      <td>ENSP00000259457</td>
-      <td>277</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>ENST00000394249.8</td>
-      <td>1863</td>
-      <td>ENST00000394249</td>
-      <td>3</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>MRRSEVLAEESIVCLQKALNHLREIWELIGIPEDQRLQRTEVVKKH...</td>
-      <td>ENSP00000377793</td>
-      <td>620</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>ENST00000361337.3</td>
-      <td>2298</td>
-      <td>ENST00000361337</td>
-      <td>2</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>MSGDHLHNDSQIEADFRLNDSHKHKDKHKDREHRHKEHKKEKDREK...</td>
-      <td>ENSP00000354522</td>
-      <td>765</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>ENST00000368328.5</td>
-      <td>267</td>
-      <td>ENST00000368328</td>
-      <td>4</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>MALSTIVSQRKQIKRKAPRGFLKRVFKRKKPQLRLEKSGDLLVHLN...</td>
-      <td>ENSP00000357311</td>
-      <td>88</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>ENST00000610426.5</td>
-      <td>783</td>
-      <td>ENST00000610426</td>
-      <td>1</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>MPQNEYIELHRKRYGYRLDYHEKKRKKESREAHERSKKAKKMIGLK...</td>
-      <td>ENSP00000483484</td>
-      <td>260</td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-    </tr>
-    <tr>
-      <th>195</th>
-      <td>ENST00000339374.11</td>
-      <td>1371</td>
-      <td>ENST00000339374</td>
-      <td>6</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>MAAALKCLLTLGRWCPGLGVAPQARALAALVPGVTQVDNKSGFLQK...</td>
-      <td>ENSP00000343041</td>
-      <td>456</td>
-    </tr>
-    <tr>
-      <th>196</th>
-      <td>ENST00000393047.8</td>
-      <td>882</td>
-      <td>ENST00000393047</td>
-      <td>3</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>MSRIPLGKVLLRNVIRHTDAHNKIQEESDMWKIRELEKQMEDAYRG...</td>
-      <td>ENSP00000376767</td>
-      <td>293</td>
-    </tr>
-    <tr>
-      <th>197</th>
-      <td>ENST00000454402.7</td>
-      <td>1023</td>
-      <td>ENST00000454402</td>
-      <td>2</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>METSALKQQEQPAATKIRNLPWVEKYRPQTLNDLISHQDILSTIQK...</td>
-      <td>ENSP00000408295</td>
-      <td>340</td>
-    </tr>
-    <tr>
-      <th>198</th>
-      <td>ENST00000254998.3</td>
-      <td>423</td>
-      <td>ENST00000254998</td>
-      <td>2</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>MASVDFKTYVDQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTATLV...</td>
-      <td>ENSP00000254998</td>
-      <td>140</td>
-    </tr>
-    <tr>
-      <th>199</th>
-      <td>ENST00000381685.10</td>
-      <td>2067</td>
-      <td>ENST00000381685</td>
-      <td>5</td>
-      <td>None</td>
-      <td>protein</td>
-      <td>MQVSSLNEVKIYSLSCGKSLPEWLSDRKKRALQKKDVDVRRRIELI...</td>
-      <td>ENSP00000371101</td>
-      <td>688</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Target Transcript</th>
+<th>Target Total Length</th>
+<th>Transcript Base</th>
+<th>version</th>
+<th>desc</th>
+<th>molecule</th>
+<th>id</th>
+<th>seq</th>
+<th>AA len</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>ENST00000259457.8</td>
+<td>834</td>
+<td>ENST00000259457</td>
+<td>3</td>
+<td>None</td>
+<td>protein</td>
+<td>ENSP00000259457</td>
+<td>MAAVSVYAPPVGGFSFDNCRRNAVLEADFAKRGYKLPKVRKTGTTI...</td>
+<td>277</td>
+</tr>
+<tr>
+<th>1</th>
+<td>ENST00000394249.8</td>
+<td>1863</td>
+<td>ENST00000394249</td>
+<td>3</td>
+<td>None</td>
+<td>protein</td>
+<td>ENSP00000377793</td>
+<td>MRRSEVLAEESIVCLQKALNHLREIWELIGIPEDQRLQRTEVVKKH...</td>
+<td>620</td>
+</tr>
+<tr>
+<th>2</th>
+<td>ENST00000361337.3</td>
+<td>2298</td>
+<td>ENST00000361337</td>
+<td>2</td>
+<td>None</td>
+<td>protein</td>
+<td>ENSP00000354522</td>
+<td>MSGDHLHNDSQIEADFRLNDSHKHKDKHKDREHRHKEHKKEKDREK...</td>
+<td>765</td>
+</tr>
+<tr>
+<th>3</th>
+<td>ENST00000368328.5</td>
+<td>267</td>
+<td>ENST00000368328</td>
+<td>4</td>
+<td>None</td>
+<td>protein</td>
+<td>ENSP00000357311</td>
+<td>MALSTIVSQRKQIKRKAPRGFLKRVFKRKKPQLRLEKSGDLLVHLN...</td>
+<td>88</td>
+</tr>
+<tr>
+<th>4</th>
+<td>ENST00000610426.5</td>
+<td>783</td>
+<td>ENST00000610426</td>
+<td>1</td>
+<td>None</td>
+<td>protein</td>
+<td>ENSP00000483484</td>
+<td>MPQNEYIELHRKRYGYRLDYHEKKRKKESREAHERSKKAKKMIGLK...</td>
+<td>260</td>
+</tr>
+<tr>
+<th>...</th>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+</tr>
+<tr>
+<th>193</th>
+<td>ENST00000339374.11</td>
+<td>1371</td>
+<td>ENST00000339374</td>
+<td>6</td>
+<td>None</td>
+<td>protein</td>
+<td>ENSP00000343041</td>
+<td>MAAALKCLLTLGRWCPGLGVAPQARALAALVPGVTQVDNKSGFLQK...</td>
+<td>456</td>
+</tr>
+<tr>
+<th>194</th>
+<td>ENST00000393047.8</td>
+<td>882</td>
+<td>ENST00000393047</td>
+<td>3</td>
+<td>None</td>
+<td>protein</td>
+<td>ENSP00000376767</td>
+<td>MSRIPLGKVLLRNVIRHTDAHNKIQEESDMWKIRELEKQMEDAYRG...</td>
+<td>293</td>
+</tr>
+<tr>
+<th>195</th>
+<td>ENST00000454402.7</td>
+<td>1023</td>
+<td>ENST00000454402</td>
+<td>2</td>
+<td>None</td>
+<td>protein</td>
+<td>ENSP00000408295</td>
+<td>METSALKQQEQPAATKIRNLPWVEKYRPQTLNDLISHQDILSTIQK...</td>
+<td>340</td>
+</tr>
+<tr>
+<th>196</th>
+<td>ENST00000254998.3</td>
+<td>423</td>
+<td>ENST00000254998</td>
+<td>2</td>
+<td>None</td>
+<td>protein</td>
+<td>ENSP00000254998</td>
+<td>MASVDFKTYVDQACRAAEEFVNVYYTTMDKRRRLLSRLYMGTATLV...</td>
+<td>140</td>
+</tr>
+<tr>
+<th>197</th>
+<td>ENST00000381685.10</td>
+<td>2067</td>
+<td>ENST00000381685</td>
+<td>5</td>
+<td>None</td>
+<td>protein</td>
+<td>ENSP00000371101</td>
+<td>MQVSSLNEVKIYSLSCGKSLPEWLSDRKKRALQKKDVDVRRRIELI...</td>
+<td>688</td>
+</tr>
+</tbody>
 </table>
-<p>200 rows × 9 columns</p>
+<p>198 rows × 9 columns</p>
+</div></div>
 </div>
 </div>
-
-</div>
-
 </div>
-</div>
-
 </div>
     {% endraw %}
 
@@ -485,15 +426,13 @@ <h2 id="Get-protein-domains">Get protein domains<a class="anchor-link" href="#Ge
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 <div class="output_wrapper">
 <div class="output">
-
 <div class="output_area">
-
-
-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="ensembl_get" class="doc_header"><code>ensembl_get</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L125" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>ensembl_get</code>(<strong><code>ext</code></strong>, <strong><code>query</code></strong>=<em><code>None</code></em>, <strong><code>headers</code></strong>=<em><code>None</code></em>, <strong><code>params</code></strong>=<em><code>None</code></em>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="ensembl_get"><code>ensembl_get</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L126" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>ensembl_get</code>(<strong><code>ext</code></strong>, <strong><code>query</code></strong>=<em><code>None</code></em>, <strong><code>headers</code></strong>=<em><code>None</code></em>, <strong><code>params</code></strong>=<em><code>None</code></em>)</p>
 </blockquote>
 <p>Generic wrapper for using GET requests to the ensembl rest API</p>
 <p>ext: str, url extension |
@@ -501,48 +440,37 @@ <h4 id="ensembl_get" class="doc_header"><code>ensembl_get</code><a href="https:/
 headers: dict or None,  meta-information for query |
 params: dict or None, parameters for query |</p>
 <p>returns: Response object</p>
-
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-
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-
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-
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     {% endraw %}
 
     {% raw %}
     
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-
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-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="get_translation_overlap" class="doc_header"><code>get_translation_overlap</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L147" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_translation_overlap</code>(<strong><code>ensembl_id</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="get_translation_overlap"><code>get_translation_overlap</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L148" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>get_translation_overlap</code>(<strong><code>ensembl_id</code></strong>)</p>
 </blockquote>
 <p>Get features that overlap with translation, such as protein domains</p>
 <p>:param ensembl_id: str
 :return: DataFrame</p>
-
 </div>
-
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-
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 </div>
-
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     {% endraw %}
 
     {% raw %}
     
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     {% endraw %}
 
@@ -550,52 +478,42 @@ <h4 id="get_translation_overlap" class="doc_header"><code>get_translation_overla
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">brca1_overlap</span> <span class="o">=</span> <span class="n">get_translation_overlap</span><span class="p">(</span><span class="s1">&#39;ENSP00000350283&#39;</span><span class="p">)</span>
-<span class="k">assert</span> <span class="s1">&#39;BRCT domain&#39;</span> <span class="ow">in</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">brca1_overlap</span><span class="p">)[</span><span class="s1">&#39;description&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">to_list</span><span class="p">()</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">brca1_overlap</span> <span class="o">=</span> <span class="n">get_translation_overlap</span><span class="p">(</span><span class="s1">'ENSP00000350283'</span><span class="p">)</span>
+<span class="k">assert</span> <span class="s1">'BRCT domain'</span> <span class="ow">in</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">brca1_overlap</span><span class="p">)[</span><span class="s1">'description'</span><span class="p">]</span><span class="o">.</span><span class="n">to_list</span><span class="p">()</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 </div>
     {% endraw %}
 
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 <div class="output_wrapper">
 <div class="output">
-
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-
-
-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="build_translation_overlap_df" class="doc_header"><code>build_translation_overlap_df</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L160" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>build_translation_overlap_df</code>(<strong><code>protein_ids</code></strong>, <strong><code>n_jobs</code></strong>=<em><code>1</code></em>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="build_translation_overlap_df"><code>build_translation_overlap_df</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L161" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>build_translation_overlap_df</code>(<strong><code>protein_ids</code></strong>, <strong><code>n_jobs</code></strong>=<em><code>1</code></em>)</p>
 </blockquote>
 <p>Get protein domain information</p>
 <p>:param protein_ids: list of str, ensemble protein IDs
 :param n_jobs: int
 :return: DataFrame</p>
-
 </div>
-
 </div>
-
 </div>
 </div>
-
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     {% endraw %}
 
     {% raw %}
     
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-
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     {% endraw %}
 
@@ -603,43 +521,32 @@ <h4 id="build_translation_overlap_df" class="doc_header"><code>build_translation
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">translation_overlap_df</span> <span class="o">=</span> <span class="n">build_translation_overlap_df</span><span class="p">(</span><span class="n">transcript_aa_seq_df</span><span class="p">[</span><span class="s1">&#39;id&#39;</span><span class="p">],</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">translation_overlap_df</span> <span class="o">=</span> <span class="n">build_translation_overlap_df</span><span class="p">(</span><span class="n">transcript_aa_seq_df</span><span class="p">[</span><span class="s1">'id'</span><span class="p">],</span>
                                                       <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 <span class="n">translation_overlap_df</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 <div class="output_wrapper">
 <div class="output">
-
 <div class="output_area">
-
 <div class="output_subarea output_stream output_stdout output_text">
 <pre>Getting protein domains
 </pre>
 </div>
 </div>
-
 <div class="output_area">
-
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████| 200/200 [00:48&lt;00:00,  4.09it/s]
+<pre>100%|██████████| 198/198 [00:49&lt;00:00,  4.00it/s]
 </pre>
 </div>
 </div>
-
 <div class="output_area">
-
-
-<div class="output_html rendered_html output_subarea output_execute_result">
-<div>
-<style scoped>
+<div class="output_html rendered_html output_subarea output_execute_result"><div>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -653,236 +560,232 @@ <h4 id="build_translation_overlap_df" class="doc_header"><code>build_translation
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>hseqname</th>
-      <th>translation_id</th>
-      <th>interpro</th>
-      <th>start</th>
-      <th>Transcript Base</th>
-      <th>align_type</th>
-      <th>feature_type</th>
-      <th>cigar_string</th>
-      <th>id</th>
-      <th>type</th>
-      <th>hit_end</th>
-      <th>end</th>
-      <th>hit_start</th>
-      <th>seq_region_name</th>
-      <th>description</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>3.60.20.10</td>
-      <td>976188</td>
-      <td>IPR029055</td>
-      <td>44</td>
-      <td>ENST00000259457</td>
-      <td>None</td>
-      <td>protein_feature</td>
-      <td></td>
-      <td>3.60.20.10</td>
-      <td>Gene3D</td>
-      <td>233</td>
-      <td>277</td>
-      <td>1</td>
-      <td>ENSP00000259457</td>
-      <td>Nucleophile aminohydrolases, N-terminal</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>PF12465</td>
-      <td>976188</td>
-      <td>IPR024689</td>
-      <td>235</td>
-      <td>ENST00000259457</td>
-      <td>None</td>
-      <td>protein_feature</td>
-      <td></td>
-      <td>PF12465</td>
-      <td>Pfam</td>
-      <td>36</td>
-      <td>271</td>
-      <td>1</td>
-      <td>ENSP00000259457</td>
-      <td>Proteasome beta subunit, C-terminal</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>PF00227</td>
-      <td>976188</td>
-      <td>IPR001353</td>
-      <td>41</td>
-      <td>ENST00000259457</td>
-      <td>None</td>
-      <td>protein_feature</td>
-      <td></td>
-      <td>PF00227</td>
-      <td>Pfam</td>
-      <td>190</td>
-      <td>221</td>
-      <td>2</td>
-      <td>ENSP00000259457</td>
-      <td>Proteasome, subunit alpha/beta</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>PR00141</td>
-      <td>976188</td>
-      <td>IPR000243</td>
-      <td>51</td>
-      <td>ENST00000259457</td>
-      <td>None</td>
-      <td>protein_feature</td>
-      <td></td>
-      <td>PR00141</td>
-      <td>PRINTS</td>
-      <td>0</td>
-      <td>66</td>
-      <td>0</td>
-      <td>ENSP00000259457</td>
-      <td>Peptidase T1A, proteasome beta-subunit</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>PR00141</td>
-      <td>976188</td>
-      <td>IPR000243</td>
-      <td>171</td>
-      <td>ENST00000259457</td>
-      <td>None</td>
-      <td>protein_feature</td>
-      <td></td>
-      <td>PR00141</td>
-      <td>PRINTS</td>
-      <td>0</td>
-      <td>182</td>
-      <td>0</td>
-      <td>ENSP00000259457</td>
-      <td>Peptidase T1A, proteasome beta-subunit</td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-    </tr>
-    <tr>
-      <th>5275</th>
-      <td>Coil</td>
-      <td>975385</td>
-      <td></td>
-      <td>516</td>
-      <td>ENST00000381685</td>
-      <td>None</td>
-      <td>protein_feature</td>
-      <td></td>
-      <td>Coil</td>
-      <td>ncoils</td>
-      <td>0</td>
-      <td>537</td>
-      <td>0</td>
-      <td>ENSP00000371101</td>
-      <td></td>
-    </tr>
-    <tr>
-      <th>5276</th>
-      <td>Coil</td>
-      <td>975385</td>
-      <td></td>
-      <td>558</td>
-      <td>ENST00000381685</td>
-      <td>None</td>
-      <td>protein_feature</td>
-      <td></td>
-      <td>Coil</td>
-      <td>ncoils</td>
-      <td>0</td>
-      <td>587</td>
-      <td>0</td>
-      <td>ENSP00000371101</td>
-      <td></td>
-    </tr>
-    <tr>
-      <th>5277</th>
-      <td>Coil</td>
-      <td>975385</td>
-      <td></td>
-      <td>640</td>
-      <td>ENST00000381685</td>
-      <td>None</td>
-      <td>protein_feature</td>
-      <td></td>
-      <td>Coil</td>
-      <td>ncoils</td>
-      <td>0</td>
-      <td>669</td>
-      <td>0</td>
-      <td>ENSP00000371101</td>
-      <td></td>
-    </tr>
-    <tr>
-      <th>5278</th>
-      <td>PTHR14927</td>
-      <td>975385</td>
-      <td>IPR040382</td>
-      <td>1</td>
-      <td>ENST00000381685</td>
-      <td>None</td>
-      <td>protein_feature</td>
-      <td></td>
-      <td>PTHR14927</td>
-      <td>PANTHER</td>
-      <td>676</td>
-      <td>682</td>
-      <td>5</td>
-      <td>ENSP00000371101</td>
-      <td>Nucleolar protein 10/Enp2</td>
-    </tr>
-    <tr>
-      <th>5279</th>
-      <td>SSF50978</td>
-      <td>975385</td>
-      <td>IPR036322</td>
-      <td>42</td>
-      <td>ENST00000381685</td>
-      <td>None</td>
-      <td>protein_feature</td>
-      <td></td>
-      <td>SSF50978</td>
-      <td>SuperFamily</td>
-      <td>0</td>
-      <td>339</td>
-      <td>0</td>
-      <td>ENSP00000371101</td>
-      <td>WD40-repeat-containing domain superfamily</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>cigar_string</th>
+<th>align_type</th>
+<th>hit_start</th>
+<th>start</th>
+<th>description</th>
+<th>hit_end</th>
+<th>hseqname</th>
+<th>seq_region_name</th>
+<th>interpro</th>
+<th>type</th>
+<th>feature_type</th>
+<th>Transcript Base</th>
+<th>translation_id</th>
+<th>end</th>
+<th>id</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td></td>
+<td>None</td>
+<td>3</td>
+<td>3</td>
+<td>Proteasome B-type subunit</td>
+<td>234</td>
+<td>PTHR32194</td>
+<td>ENSP00000259457</td>
+<td>IPR023333</td>
+<td>PANTHER</td>
+<td>protein_feature</td>
+<td>ENST00000259457</td>
+<td>1109135</td>
+<td>237</td>
+<td>PTHR32194</td>
+</tr>
+<tr>
+<th>1</th>
+<td></td>
+<td>None</td>
+<td>1</td>
+<td>44</td>
+<td>Via SIFTS (2025/02/17) UniProt protein Q99436 ...</td>
+<td>220</td>
+<td>4r3o.I</td>
+<td>ENSP00000259457</td>
+<td></td>
+<td>sifts</td>
+<td>protein_feature</td>
+<td>ENST00000259457</td>
+<td>1109135</td>
+<td>263</td>
+<td>4r3o.I</td>
+</tr>
+<tr>
+<th>2</th>
+<td></td>
+<td>None</td>
+<td>1</td>
+<td>44</td>
+<td>Via SIFTS (2025/02/17) UniProt protein Q99436 ...</td>
+<td>220</td>
+<td>4r3o.W</td>
+<td>ENSP00000259457</td>
+<td></td>
+<td>sifts</td>
+<td>protein_feature</td>
+<td>ENST00000259457</td>
+<td>1109135</td>
+<td>263</td>
+<td>4r3o.W</td>
+</tr>
+<tr>
+<th>3</th>
+<td></td>
+<td>None</td>
+<td>1</td>
+<td>44</td>
+<td>Via SIFTS (2025/02/17) UniProt protein Q99436 ...</td>
+<td>220</td>
+<td>4r67.I</td>
+<td>ENSP00000259457</td>
+<td></td>
+<td>sifts</td>
+<td>protein_feature</td>
+<td>ENST00000259457</td>
+<td>1109135</td>
+<td>263</td>
+<td>4r67.I</td>
+</tr>
+<tr>
+<th>4</th>
+<td></td>
+<td>None</td>
+<td>1</td>
+<td>44</td>
+<td>Via SIFTS (2025/02/17) UniProt protein Q99436 ...</td>
+<td>220</td>
+<td>4r67.W</td>
+<td>ENSP00000259457</td>
+<td></td>
+<td>sifts</td>
+<td>protein_feature</td>
+<td>ENST00000259457</td>
+<td>1109135</td>
+<td>263</td>
+<td>4r67.W</td>
+</tr>
+<tr>
+<th>...</th>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+</tr>
+<tr>
+<th>7675</th>
+<td></td>
+<td>None</td>
+<td>0</td>
+<td>558</td>
+<td>Coil</td>
+<td>0</td>
+<td>Coil</td>
+<td>ENSP00000371101</td>
+<td></td>
+<td>ncoils</td>
+<td>protein_feature</td>
+<td>ENST00000381685</td>
+<td>1303115</td>
+<td>587</td>
+<td>Coil</td>
+</tr>
+<tr>
+<th>7676</th>
+<td></td>
+<td>None</td>
+<td>0</td>
+<td>516</td>
+<td>Coil</td>
+<td>0</td>
+<td>Coil</td>
+<td>ENSP00000371101</td>
+<td></td>
+<td>ncoils</td>
+<td>protein_feature</td>
+<td>ENST00000381685</td>
+<td>1303115</td>
+<td>537</td>
+<td>Coil</td>
+</tr>
+<tr>
+<th>7677</th>
+<td></td>
+<td>None</td>
+<td>0</td>
+<td>426</td>
+<td>Coil</td>
+<td>0</td>
+<td>Coil</td>
+<td>ENSP00000371101</td>
+<td></td>
+<td>ncoils</td>
+<td>protein_feature</td>
+<td>ENST00000381685</td>
+<td>1303115</td>
+<td>446</td>
+<td>Coil</td>
+</tr>
+<tr>
+<th>7678</th>
+<td></td>
+<td>None</td>
+<td>1</td>
+<td>1</td>
+<td>Nucleolar protein 10/Enp2</td>
+<td>655</td>
+<td>PTHR14927</td>
+<td>ENSP00000371101</td>
+<td>IPR040382</td>
+<td>PANTHER</td>
+<td>protein_feature</td>
+<td>ENST00000381685</td>
+<td>1303115</td>
+<td>684</td>
+<td>PTHR14927</td>
+</tr>
+<tr>
+<th>7679</th>
+<td></td>
+<td>None</td>
+<td>0</td>
+<td>42</td>
+<td>WD40-repeat-containing domain superfamily</td>
+<td>0</td>
+<td>SSF50978</td>
+<td>ENSP00000371101</td>
+<td>IPR036322</td>
+<td>SuperFamily</td>
+<td>protein_feature</td>
+<td>ENST00000381685</td>
+<td>1303115</td>
+<td>339</td>
+<td>SSF50978</td>
+</tr>
+</tbody>
 </table>
-<p>5280 rows × 15 columns</p>
-</div>
+<p>7680 rows × 15 columns</p>
+</div></div>
 </div>
-
 </div>
-
 </div>
-</div>
-
 </div>
     {% endraw %}
 
@@ -895,15 +798,13 @@ <h2 id="Store-data-using-parquet-for-later-use">Store data using parquet for lat
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 <div class="output_wrapper">
 <div class="output">
-
 <div class="output_area">
-
-
-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="write_transcript_data" class="doc_header"><code>write_transcript_data</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L176" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>write_transcript_data</code>(<strong><code>design_df</code></strong>, <strong><code>transcript_id_col</code></strong>=<em><code>'Target Transcript'</code></em>, <strong><code>transcript_len_col</code></strong>=<em><code>'Target Total Length'</code></em>, <strong><code>n_jobs</code></strong>=<em><code>1</code></em>, <strong><code>overwrite</code></strong>=<em><code>True</code></em>, <strong><code>filepath</code></strong>=<em><code>'./data/target_data/'</code></em>, <strong><code>aa_seq_name</code></strong>=<em><code>'aa_seqs.pq'</code></em>, <strong><code>protein_domain_name</code></strong>=<em><code>'protein_domains.pq'</code></em>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="write_transcript_data"><code>write_transcript_data</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L177" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>write_transcript_data</code>(<strong><code>design_df</code></strong>, <strong><code>transcript_id_col</code></strong>=<em><code>'Target Transcript'</code></em>, <strong><code>transcript_len_col</code></strong>=<em><code>'Target Total Length'</code></em>, <strong><code>n_jobs</code></strong>=<em><code>1</code></em>, <strong><code>overwrite</code></strong>=<em><code>True</code></em>, <strong><code>filepath</code></strong>=<em><code>'./data/target_data/'</code></em>, <strong><code>aa_seq_name</code></strong>=<em><code>'aa_seqs.pq'</code></em>, <strong><code>protein_domain_name</code></strong>=<em><code>'protein_domains.pq'</code></em>)</p>
 </blockquote>
 <p>Write amino acid sequences and protein domain information to parquet files</p>
 <p>:param design_df: DataFrame
@@ -914,21 +815,16 @@ <h4 id="write_transcript_data" class="doc_header"><code>write_transcript_data</c
 :param filepath: str, directory for output sequences
 :param aa_seq_name: str, name of amino acid sequence file
 :param protein_domain_name: str, name of protein domain file</p>
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@@ -936,99 +832,80 @@ <h4 id="write_transcript_data" class="doc_header"><code>write_transcript_data</c
     
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">write_transcript_data</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-<span class="k">assert</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="s1">&#39;./data/target_data/&#39;</span> <span class="o">+</span> <span class="s1">&#39;aa_seqs.pq&#39;</span><span class="p">)</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">write_transcript_data</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="k">assert</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="s1">'./data/target_data/'</span> <span class="o">+</span> <span class="s1">'aa_seqs.pq'</span><span class="p">)</span>
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 <pre>Getting amino acid sequences
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-<pre>100%|██████████| 4/4 [00:02&lt;00:00,  1.42it/s]
+<pre>100%|██████████| 4/4 [00:01&lt;00:00,  2.11it/s]
+/var/folders/n9/c397fj3n5f16khr7tp76tbpc0000gn/T/ipykernel_37465/1237391668.py:23: UserWarning: Unable to find translations for the following transcripts: ENST00000629399, ENST00000284049
+  warnings.warn('Unable to find translations for the following transcripts: ' + ', '.join(missing_seqs))
 </pre>
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 <pre>Getting protein domains
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-<pre>100%|██████████| 200/200 [00:48&lt;00:00,  4.13it/s]
+<pre>100%|██████████| 198/198 [00:50&lt;00:00,  3.95it/s]
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 <h1 id="Get-Conservation-Scores">Get Conservation Scores<a class="anchor-link" href="#Get-Conservation-Scores"> </a></h1><p>Get PhyloP conservation scores</p>
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-<h4 id="get_transcript_info" class="doc_header"><code>get_transcript_info</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L209" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_transcript_info</code>(<strong><code>base_transcript</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="get_transcript_info"><code>get_transcript_info</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L210" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>get_transcript_info</code>(<strong><code>base_transcript</code></strong>)</p>
 </blockquote>
 <p>Using an ensembl transcript ID, get</p>
 <p>:param base_transcript: str
 :return: (exon_df, trans_sr, chr)
-    exon_df: DataFrame, with global exon start and end position
-    trans_sr: Series, with global translation start and stop positions for CDS and translation length
-    chr: str</p>
-
+exon_df: DataFrame, with global exon start and end position
+trans_sr: Series, with global translation start and stop positions for CDS and translation length
+chr: str</p>
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@@ -1036,34 +913,29 @@ <h4 id="get_transcript_info" class="doc_header"><code>get_transcript_info</code>
     
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">exon_df</span><span class="p">,</span> <span class="n">trans_sr</span><span class="p">,</span> <span class="nb">chr</span> <span class="o">=</span> <span class="n">get_transcript_info</span><span class="p">(</span><span class="s1">&#39;ENST00000259457&#39;</span><span class="p">)</span>
-<span class="k">assert</span> <span class="nb">chr</span> <span class="o">==</span> <span class="s1">&#39;9&#39;</span>
-<span class="k">assert</span> <span class="n">trans_sr</span><span class="p">[</span><span class="s1">&#39;length&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">277</span> <span class="c1"># number of aas</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">exon_df</span><span class="p">,</span> <span class="n">trans_sr</span><span class="p">,</span> <span class="nb">chr</span> <span class="o">=</span> <span class="n">get_transcript_info</span><span class="p">(</span><span class="s1">'ENST00000259457'</span><span class="p">)</span>
+<span class="k">assert</span> <span class="nb">chr</span> <span class="o">==</span> <span class="s1">'9'</span>
+<span class="k">assert</span> <span class="n">trans_sr</span><span class="p">[</span><span class="s1">'length'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">277</span> <span class="c1"># number of aas</span>
 <span class="k">assert</span> <span class="n">exon_df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">8</span> <span class="c1"># eight exons</span>
 </pre></div>
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-<h4 id="get_conservation" class="doc_header"><code>get_conservation</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L228" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_conservation</code>(<strong><code>chr</code></strong>, <strong><code>start</code></strong>, <strong><code>end</code></strong>, <strong><code>genome</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="get_conservation"><code>get_conservation</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L229" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>get_conservation</code>(<strong><code>chr</code></strong>, <strong><code>start</code></strong>, <strong><code>end</code></strong>, <strong><code>genome</code></strong>)</p>
 </blockquote>
 <p>Get conservation scores for a given region of a genome</p>
 <p>:param chr: str, chromosome number
@@ -1071,21 +943,16 @@ <h4 id="get_conservation" class="doc_header"><code>get_conservation</code><a hre
 :param end: int
 :param genome: str
 :return: DataFrame</p>
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@@ -1093,83 +960,67 @@ <h4 id="get_conservation" class="doc_header"><code>get_conservation</code><a hre
     
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rps20_exon_conservation</span> <span class="o">=</span> <span class="n">get_conservation</span><span class="p">(</span><span class="s1">&#39;8&#39;</span><span class="p">,</span> <span class="mi">56074060</span><span class="p">,</span> <span class="mi">56074159</span><span class="p">,</span> <span class="s1">&#39;hg38&#39;</span><span class="p">)</span>
-<span class="n">cd274_exon_conservation</span> <span class="o">=</span> <span class="n">get_conservation</span><span class="p">(</span><span class="s1">&#39;9&#39;</span><span class="p">,</span> <span class="mi">5457079</span><span class="p">,</span> <span class="mi">5457420</span><span class="p">,</span> <span class="s1">&#39;hg38&#39;</span><span class="p">)</span>
-<span class="k">assert</span> <span class="n">rps20_exon_conservation</span><span class="p">[</span><span class="s1">&#39;conservation&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">&gt;</span> <span class="n">cd274_exon_conservation</span><span class="p">[</span><span class="s1">&#39;conservation&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">rps20_exon_conservation</span> <span class="o">=</span> <span class="n">get_conservation</span><span class="p">(</span><span class="s1">'8'</span><span class="p">,</span> <span class="mi">56074060</span><span class="p">,</span> <span class="mi">56074159</span><span class="p">,</span> <span class="s1">'hg38'</span><span class="p">)</span>
+<span class="n">cd274_exon_conservation</span> <span class="o">=</span> <span class="n">get_conservation</span><span class="p">(</span><span class="s1">'9'</span><span class="p">,</span> <span class="mi">5457079</span><span class="p">,</span> <span class="mi">5457420</span><span class="p">,</span> <span class="s1">'hg38'</span><span class="p">)</span>
+<span class="k">assert</span> <span class="n">rps20_exon_conservation</span><span class="p">[</span><span class="s1">'conservation'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">&gt;</span> <span class="n">cd274_exon_conservation</span><span class="p">[</span><span class="s1">'conservation'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
 </pre></div>
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-<h4 id="get_exon_conservation" class="doc_header"><code>get_exon_conservation</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L261" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_exon_conservation</code>(<strong><code>exon_df</code></strong>, <strong><code>chr</code></strong>, <strong><code>genome</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="get_exon_conservation"><code>get_exon_conservation</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L262" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>get_exon_conservation</code>(<strong><code>exon_df</code></strong>, <strong><code>chr</code></strong>, <strong><code>genome</code></strong>)</p>
 </blockquote>
 <p>Get conservation scores for each exon</p>
 <p>:param exon_df: DataFrame
 :param chr: str
 :param genome: str
 :return: DataFrame</p>
-
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-<h4 id="get_transcript_conservation" class="doc_header"><code>get_transcript_conservation</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L283" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_transcript_conservation</code>(<strong><code>transcript_id</code></strong>, <strong><code>target_strand</code></strong>, <strong><code>genome</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="get_transcript_conservation"><code>get_transcript_conservation</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L284" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>get_transcript_conservation</code>(<strong><code>transcript_id</code></strong>, <strong><code>target_strand</code></strong>, <strong><code>genome</code></strong>)</p>
 </blockquote>
 <p>Get conservation scores for a transcript</p>
 <p>:param transcript_id: str
 :param target_strand: str, '+' or '-'
 :param genome: str
 :return: DataFrame</p>
-
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@@ -1177,74 +1028,58 @@ <h4 id="get_transcript_conservation" class="doc_header"><code>get_transcript_con
     
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rps20_conservation_df</span> <span class="o">=</span> <span class="n">get_transcript_conservation</span><span class="p">(</span><span class="s1">&#39;ENST00000009589&#39;</span><span class="p">,</span> <span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;hg38&#39;</span><span class="p">)</span>
-<span class="n">cd274_conservation_df</span> <span class="o">=</span> <span class="n">get_transcript_conservation</span><span class="p">(</span><span class="s1">&#39;ENST00000381577&#39;</span><span class="p">,</span> <span class="s1">&#39;+&#39;</span><span class="p">,</span> <span class="s1">&#39;hg38&#39;</span><span class="p">)</span>
-<span class="k">assert</span> <span class="n">rps20_conservation_df</span><span class="p">[</span><span class="s1">&#39;conservation&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">&gt;</span> <span class="n">cd274_conservation_df</span><span class="p">[</span><span class="s1">&#39;conservation&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">rps20_conservation_df</span> <span class="o">=</span> <span class="n">get_transcript_conservation</span><span class="p">(</span><span class="s1">'ENST00000009589'</span><span class="p">,</span> <span class="s1">'-'</span><span class="p">,</span> <span class="s1">'hg38'</span><span class="p">)</span>
+<span class="n">cd274_conservation_df</span> <span class="o">=</span> <span class="n">get_transcript_conservation</span><span class="p">(</span><span class="s1">'ENST00000381577'</span><span class="p">,</span> <span class="s1">'+'</span><span class="p">,</span> <span class="s1">'hg38'</span><span class="p">)</span>
+<span class="k">assert</span> <span class="n">rps20_conservation_df</span><span class="p">[</span><span class="s1">'conservation'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">&gt;</span> <span class="n">cd274_conservation_df</span><span class="p">[</span><span class="s1">'conservation'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
 </pre></div>
-
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-<h4 id="get_transcript_conservation_safe" class="doc_header"><code>get_transcript_conservation_safe</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L312" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_transcript_conservation_safe</code>(<strong><code>transcript_id</code></strong>, <strong><code>target_strand</code></strong>, <strong><code>genome</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="get_transcript_conservation_safe"><code>get_transcript_conservation_safe</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L313" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>get_transcript_conservation_safe</code>(<strong><code>transcript_id</code></strong>, <strong><code>target_strand</code></strong>, <strong><code>genome</code></strong>)</p>
 </blockquote>
 <p>Helper function for parrallel query. Return None when conservation dataframe cannot be assembled</p>
-
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-<h4 id="build_conservation_df" class="doc_header"><code>build_conservation_df</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L320" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>build_conservation_df</code>(<strong><code>design_df</code></strong>, <strong><code>n_jobs</code></strong>=<em><code>1</code></em>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="build_conservation_df"><code>build_conservation_df</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L321" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>build_conservation_df</code>(<strong><code>design_df</code></strong>, <strong><code>n_jobs</code></strong>=<em><code>1</code></em>)</p>
 </blockquote>
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@@ -1252,62 +1087,61 @@ <h4 id="build_conservation_df" class="doc_header"><code>build_conservation_df</c
     
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">conservation_df</span> <span class="o">=</span> <span class="n">build_conservation_df</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">max_n_jobs</span><span class="p">)</span>
-<span class="k">assert</span> <span class="p">(</span><span class="n">conservation_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;Target Transcript&#39;</span><span class="p">)</span>
-        <span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">df</span><span class="p">:</span> <span class="n">stats</span><span class="o">.</span><span class="n">spearmanr</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;conservation&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;ranked_conservation&#39;</span><span class="p">])[</span><span class="mi">0</span><span class="p">])</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">conservation_df</span> <span class="o">=</span> <span class="n">build_conservation_df</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">max_n_jobs</span><span class="p">)</span>
+<span class="k">assert</span> <span class="p">(</span><span class="n">conservation_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'Target Transcript'</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">df</span><span class="p">:</span> <span class="n">stats</span><span class="o">.</span><span class="n">spearmanr</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'conservation'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'ranked_conservation'</span><span class="p">])[</span><span class="mi">0</span><span class="p">])</span>
         <span class="o">&gt;</span> <span class="mf">0.99</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
-<span class="k">assert</span> <span class="p">(</span><span class="n">conservation_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">conservation_df</span><span class="p">[</span><span class="s1">&#39;Transcript Base&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;ENST00000361337&#39;</span><span class="p">)</span> <span class="o">&amp;</span>
-                            <span class="p">(</span><span class="n">conservation_df</span><span class="p">[</span><span class="s1">&#39;target position&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">between</span><span class="p">(</span><span class="mi">432</span><span class="o">*</span><span class="mi">3</span><span class="p">,</span> <span class="mi">663</span><span class="o">*</span><span class="mi">3</span><span class="p">)),</span>
-                            <span class="s1">&#39;ranked_conservation&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">&gt;</span>  <span class="c1"># pfam</span>
-        <span class="n">conservation_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">conservation_df</span><span class="p">[</span><span class="s1">&#39;Transcript Base&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;ENST00000361337&#39;</span><span class="p">)</span> <span class="o">&amp;</span>
-                            <span class="p">(</span><span class="n">conservation_df</span><span class="p">[</span><span class="s1">&#39;target position&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">between</span><span class="p">(</span><span class="mi">36</span><span class="o">*</span><span class="mi">3</span><span class="p">,</span> <span class="mi">199</span><span class="o">*</span><span class="mi">3</span><span class="p">)),</span>  <span class="c1"># mobidblit</span>
-                            <span class="s1">&#39;ranked_conservation&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
+<span class="k">assert</span> <span class="p">(</span><span class="n">conservation_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">conservation_df</span><span class="p">[</span><span class="s1">'Transcript Base'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'ENST00000361337'</span><span class="p">)</span> <span class="o">&amp;</span>
+                            <span class="p">(</span><span class="n">conservation_df</span><span class="p">[</span><span class="s1">'target position'</span><span class="p">]</span><span class="o">.</span><span class="n">between</span><span class="p">(</span><span class="mi">432</span><span class="o">*</span><span class="mi">3</span><span class="p">,</span> <span class="mi">663</span><span class="o">*</span><span class="mi">3</span><span class="p">)),</span>
+                            <span class="s1">'ranked_conservation'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">&gt;</span>  <span class="c1"># pfam</span>
+        <span class="n">conservation_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">conservation_df</span><span class="p">[</span><span class="s1">'Transcript Base'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'ENST00000361337'</span><span class="p">)</span> <span class="o">&amp;</span>
+                            <span class="p">(</span><span class="n">conservation_df</span><span class="p">[</span><span class="s1">'target position'</span><span class="p">]</span><span class="o">.</span><span class="n">between</span><span class="p">(</span><span class="mi">36</span><span class="o">*</span><span class="mi">3</span><span class="p">,</span> <span class="mi">199</span><span class="o">*</span><span class="mi">3</span><span class="p">)),</span>  <span class="c1"># mobidblit</span>
+                            <span class="s1">'ranked_conservation'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
 </pre></div>
-
-    </div>
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+</div>
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-
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 <pre>Getting conservation
 </pre>
 </div>
 </div>
-
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-
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████| 200/200 [03:33&lt;00:00,  1.07s/it]
+<pre></pre>
+</div>
+</div>
+<div class="output_area">
+<div class="output_subarea output_stream output_stderr output_text">
+<pre> 84%|████████▎ | 167/200 [05:05&lt;01:00,  1.83s/it]
+100%|██████████| 200/200 [05:20&lt;00:00,  1.60s/it]
+/var/folders/n9/c397fj3n5f16khr7tp76tbpc0000gn/T/ipykernel_37465/986071231.py:35: UserWarning: Failed to get conservation scores for 2 transcriptsENST00000629399, ENST00000284049
+  warnings.warn('Failed to get conservation scores for ' + str(len(failed_list)) +
+/var/folders/n9/c397fj3n5f16khr7tp76tbpc0000gn/T/ipykernel_37465/3931529888.py:2: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
+  assert (conservation_df.groupby('Target Transcript')
 </pre>
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-
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-
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-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="write_conservation_data" class="doc_header"><code>write_conservation_data</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L364" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>write_conservation_data</code>(<strong><code>design_df</code></strong>, <strong><code>n_jobs</code></strong>=<em><code>1</code></em>, <strong><code>overwrite</code></strong>=<em><code>True</code></em>, <strong><code>filepath</code></strong>=<em><code>'./data/target_data/'</code></em>, <strong><code>cons_file_name</code></strong>=<em><code>'conservation.pq'</code></em>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="write_conservation_data"><code>write_conservation_data</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetdata.py#L365" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>write_conservation_data</code>(<strong><code>design_df</code></strong>, <strong><code>n_jobs</code></strong>=<em><code>1</code></em>, <strong><code>overwrite</code></strong>=<em><code>True</code></em>, <strong><code>filepath</code></strong>=<em><code>'./data/target_data/'</code></em>, <strong><code>cons_file_name</code></strong>=<em><code>'conservation.pq'</code></em>)</p>
 </blockquote>
 <p>Write conservation scores to parquet files</p>
 <p>:param design_df: DataFrame
@@ -1315,21 +1149,16 @@ <h4 id="write_conservation_data" class="doc_header"><code>write_conservation_dat
 :param overwrite: bool, whether to overwrite existing file
 :param filepath: str, directory for output sequences
 :param cons_file_name: str, name of conservation file</p>
-
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-
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     {% endraw %}
 
@@ -1337,42 +1166,33 @@ <h4 id="write_conservation_data" class="doc_header"><code>write_conservation_dat
     
 <div class="cell border-box-sizing code_cell rendered">
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-
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-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">write_conservation_data</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">max_n_jobs</span><span class="p">)</span>
-<span class="k">assert</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="s1">&#39;./data/target_data/&#39;</span> <span class="o">+</span> <span class="s1">&#39;conservation.pq&#39;</span><span class="p">)</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">write_conservation_data</span><span class="p">(</span><span class="n">design_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">max_n_jobs</span><span class="p">)</span>
+<span class="k">assert</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="s1">'./data/target_data/'</span> <span class="o">+</span> <span class="s1">'conservation.pq'</span><span class="p">)</span>
 </pre></div>
-
-    </div>
 </div>
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+</div>
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-
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 <pre>Getting conservation
 </pre>
 </div>
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-
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-
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████| 200/200 [05:16&lt;00:00,  1.58s/it]
+<pre>100%|██████████| 200/200 [06:05&lt;00:00,  1.83s/it]
+/var/folders/n9/c397fj3n5f16khr7tp76tbpc0000gn/T/ipykernel_37465/986071231.py:35: UserWarning: Failed to get conservation scores for 2 transcriptsENST00000629399, ENST00000284049
+  warnings.warn('Failed to get conservation scores for ' + str(len(failed_list)) +
 </pre>
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- 
-
diff --git a/docs/targetfeat.html b/docs/targetfeat.html
index 9617127..ebe99f5 100644
--- a/docs/targetfeat.html
+++ b/docs/targetfeat.html
@@ -19,20 +19,17 @@
 # command to build the docs after a change: nbdev_build_docs
 
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-
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@@ -40,16 +37,13 @@
     
 <div class="cell border-box-sizing code_cell rendered">
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-
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-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rs3</span> <span class="kn">import</span> <span class="n">targetdata</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">rs3</span><span class="w"> </span><span class="kn">import</span> <span class="n">targetdata</span>
 </pre></div>
-
-    </div>
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-
+</div>
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     {% endraw %}
 
@@ -57,51 +51,41 @@
     
 <div class="cell border-box-sizing code_cell rendered">
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-
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-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">multiprocessing</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">multiprocessing</span>
 <span class="n">max_n_jobs</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span>
 </pre></div>
-
-    </div>
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+</div>
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     {% raw %}
     
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-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="add_target_columns" class="doc_header"><code>add_target_columns</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L14" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>add_target_columns</code>(<strong><code>design_df</code></strong>, <strong><code>transcript_id_col</code></strong>=<em><code>'Target Transcript'</code></em>, <strong><code>cut_pos_col</code></strong>=<em><code>'Target Cut Length'</code></em>, <strong><code>transcript_base_col</code></strong>=<em><code>'Transcript Base'</code></em>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="add_target_columns"><code>add_target_columns</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L14" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>add_target_columns</code>(<strong><code>design_df</code></strong>, <strong><code>transcript_id_col</code></strong>=<em><code>'Target Transcript'</code></em>, <strong><code>cut_pos_col</code></strong>=<em><code>'Target Cut Length'</code></em>, <strong><code>transcript_base_col</code></strong>=<em><code>'Transcript Base'</code></em>)</p>
 </blockquote>
 <p>Add ['AA Index' and 'Transcript Base'] to design df</p>
 <p>:param design_df: DataFrame
 :return: DataFrame</p>
-
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@@ -109,67 +93,55 @@ <h4 id="add_target_columns" class="doc_header"><code>add_target_columns</code><a
     
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-
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-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">design_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">&#39;test_data/sgrna-designs.txt&#39;</span><span class="p">)</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">design_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">'test_data/sgrna-designs.txt'</span><span class="p">)</span>
 <span class="n">design_targ_df</span> <span class="o">=</span> <span class="n">add_target_columns</span><span class="p">(</span><span class="n">design_df</span><span class="p">)</span>
-<span class="k">assert</span> <span class="s1">&#39;AA Index&#39;</span> <span class="ow">in</span> <span class="n">design_targ_df</span><span class="o">.</span><span class="n">columns</span>
+<span class="k">assert</span> <span class="s1">'AA Index'</span> <span class="ow">in</span> <span class="n">design_targ_df</span><span class="o">.</span><span class="n">columns</span>
 </pre></div>
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+</div>
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 <h2 id="Position-Features">Position Features<a class="anchor-link" href="#Position-Features"> </a></h2><p>The first feature class we consider is where the guide targets within the annotated transcript</p>
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-<h4 id="get_position_features" class="doc_header"><code>get_position_features</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L28" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_position_features</code>(<strong><code>sg_df</code></strong>, <strong><code>id_cols</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="get_position_features"><code>get_position_features</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L28" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>get_position_features</code>(<strong><code>sg_df</code></strong>, <strong><code>id_cols</code></strong>)</p>
 </blockquote>
 <p>Get  features ['Target Cut %', 'sense']</p>
 <p>:param sg_df: DataFrame
 :param id_cols: list
 :return: DataFrame</p>
-
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 <h2 id="Amino-Acid-Features">Amino Acid Features<a class="anchor-link" href="#Amino-Acid-Features"> </a></h2><p>We calculate a set of features from the amino acid sequence around the cutsite itself</p>
-
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@@ -177,54 +149,44 @@ <h2 id="Amino-Acid-Features">Amino Acid Features<a class="anchor-link" href="#Am
     
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">aas</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">,</span> <span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="s1">&#39;E&#39;</span><span class="p">,</span> <span class="s1">&#39;F&#39;</span><span class="p">,</span>
-       <span class="s1">&#39;G&#39;</span><span class="p">,</span> <span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="s1">&#39;I&#39;</span><span class="p">,</span> <span class="s1">&#39;K&#39;</span><span class="p">,</span> <span class="s1">&#39;L&#39;</span><span class="p">,</span>
-       <span class="s1">&#39;M&#39;</span><span class="p">,</span> <span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;Q&#39;</span><span class="p">,</span> <span class="s1">&#39;R&#39;</span><span class="p">,</span>
-       <span class="s1">&#39;S&#39;</span><span class="p">,</span> <span class="s1">&#39;T&#39;</span><span class="p">,</span> <span class="s1">&#39;V&#39;</span><span class="p">,</span> <span class="s1">&#39;W&#39;</span><span class="p">,</span> <span class="s1">&#39;Y&#39;</span><span class="p">,</span> <span class="s1">&#39;*&#39;</span><span class="p">]</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">aas</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'D'</span><span class="p">,</span> <span class="s1">'E'</span><span class="p">,</span> <span class="s1">'F'</span><span class="p">,</span>
+       <span class="s1">'G'</span><span class="p">,</span> <span class="s1">'H'</span><span class="p">,</span> <span class="s1">'I'</span><span class="p">,</span> <span class="s1">'K'</span><span class="p">,</span> <span class="s1">'L'</span><span class="p">,</span>
+       <span class="s1">'M'</span><span class="p">,</span> <span class="s1">'N'</span><span class="p">,</span> <span class="s1">'P'</span><span class="p">,</span> <span class="s1">'Q'</span><span class="p">,</span> <span class="s1">'R'</span><span class="p">,</span>
+       <span class="s1">'S'</span><span class="p">,</span> <span class="s1">'T'</span><span class="p">,</span> <span class="s1">'V'</span><span class="p">,</span> <span class="s1">'W'</span><span class="p">,</span> <span class="s1">'Y'</span><span class="p">,</span> <span class="s1">'*'</span><span class="p">]</span>
 </pre></div>
-
-    </div>
 </div>
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-
+</div>
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     {% endraw %}
 
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
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-
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-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="get_one_aa_frac" class="doc_header"><code>get_one_aa_frac</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L40" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_one_aa_frac</code>(<strong><code>feature_dict</code></strong>, <strong><code>aa_sequence</code></strong>, <strong><code>aas</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="get_one_aa_frac"><code>get_one_aa_frac</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L40" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>get_one_aa_frac</code>(<strong><code>feature_dict</code></strong>, <strong><code>aa_sequence</code></strong>, <strong><code>aas</code></strong>)</p>
 </blockquote>
 <p>Get fraction of single aa</p>
 <p>:param feature_dict: dict, feature dictionary
 :param aa_sequence: str, amino acid sequence
 :param aas: list, list of amino acids</p>
-
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-
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-
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     {% raw %}
     
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     {% endraw %}
 
@@ -232,139 +194,111 @@ <h4 id="get_one_aa_frac" class="doc_header"><code>get_one_aa_frac</code><a href=
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">one_aa_ft</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="n">get_one_aa_frac</span><span class="p">(</span><span class="n">one_aa_ft</span><span class="p">,</span> <span class="s1">&#39;ACDG*-&#39;</span><span class="p">,</span> <span class="n">aas</span><span class="p">)</span>
-<span class="k">assert</span> <span class="n">one_aa_ft</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="o">/</span><span class="mi">6</span>
-<span class="k">assert</span> <span class="n">one_aa_ft</span><span class="p">[</span><span class="s1">&#39;Q&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">one_aa_ft</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="n">get_one_aa_frac</span><span class="p">(</span><span class="n">one_aa_ft</span><span class="p">,</span> <span class="s1">'ACDG*-'</span><span class="p">,</span> <span class="n">aas</span><span class="p">)</span>
+<span class="k">assert</span> <span class="n">one_aa_ft</span><span class="p">[</span><span class="s1">'A'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="o">/</span><span class="mi">6</span>
+<span class="k">assert</span> <span class="n">one_aa_ft</span><span class="p">[</span><span class="s1">'Q'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 </div>
     {% endraw %}
 
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 <div class="output_wrapper">
 <div class="output">
-
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-
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-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="get_aa_aromaticity" class="doc_header"><code>get_aa_aromaticity</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L52" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_aa_aromaticity</code>(<strong><code>feature_dict</code></strong>, <strong><code>analyzed_seq</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="get_aa_aromaticity"><code>get_aa_aromaticity</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L52" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>get_aa_aromaticity</code>(<strong><code>feature_dict</code></strong>, <strong><code>analyzed_seq</code></strong>)</p>
 </blockquote>
 <p>Get fraction of aromatic amino acids in a sequence.</p>
 <p>Phe (F) + Trp (W) + Tyr (Y)</p>
 <p>:param feature_dict:
 :param analyzed_seq: ProteinAnalysis object</p>
-
 </div>
-
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-
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-
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     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
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-
-
-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="get_aa_hydrophobicity" class="doc_header"><code>get_aa_hydrophobicity</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L63" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_aa_hydrophobicity</code>(<strong><code>feature_dict</code></strong>, <strong><code>analyzed_seq</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="get_aa_hydrophobicity"><code>get_aa_hydrophobicity</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L63" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>get_aa_hydrophobicity</code>(<strong><code>feature_dict</code></strong>, <strong><code>analyzed_seq</code></strong>)</p>
 </blockquote>
 <p>Grand Average of Hydropathy</p>
 <p>The GRAVY value is calculated by adding the hydropathy value for each residue and dividing
- by the length of the sequence (Kyte and Doolittle; 1982). The larger the number, the more hydrophobic the
- amino acid</p>
+by the length of the sequence (Kyte and Doolittle; 1982). The larger the number, the more hydrophobic the
+amino acid</p>
 <p>:param feature_dict: dict
 :param analyzed_seq: ProteinAnalysis object</p>
-
 </div>
-
 </div>
-
 </div>
 </div>
-
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     {% endraw %}
 
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 <div class="output_wrapper">
 <div class="output">
-
 <div class="output_area">
-
-
-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="get_aa_ip" class="doc_header"><code>get_aa_ip</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L76" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_aa_ip</code>(<strong><code>feature_dict</code></strong>, <strong><code>analyzed_seq</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="get_aa_ip"><code>get_aa_ip</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L76" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>get_aa_ip</code>(<strong><code>feature_dict</code></strong>, <strong><code>analyzed_seq</code></strong>)</p>
 </blockquote>
 <p>Get the Isoelectric Point of an amino acid sequence</p>
 <p>Charge of amino acid</p>
 <p>:param feature_dict: dict
 :param analyzed_seq: ProteinAnalysis object</p>
-
 </div>
-
 </div>
-
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 </div>
-
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     {% endraw %}
 
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
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 <div class="output">
-
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-
-
-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="get_aa_secondary_structure" class="doc_header"><code>get_aa_secondary_structure</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L87" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_aa_secondary_structure</code>(<strong><code>feature_dict</code></strong>, <strong><code>analyzed_seq</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="get_aa_secondary_structure"><code>get_aa_secondary_structure</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L87" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>get_aa_secondary_structure</code>(<strong><code>feature_dict</code></strong>, <strong><code>analyzed_seq</code></strong>)</p>
 </blockquote>
 <p>Get the fraction of amion acids that tend to be in a helix, turn or sheet</p>
 <p>:param feature_dict: dict
 :param analyzed_seq: ProteinAnalysis object</p>
-
 </div>
-
 </div>
-
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-
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     {% endraw %}
 
     {% raw %}
     
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     {% endraw %}
 
@@ -372,55 +306,45 @@ <h4 id="get_aa_secondary_structure" class="doc_header"><code>get_aa_secondary_st
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">aa_biochemical_fts1</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="n">get_aa_aromaticity</span><span class="p">(</span><span class="n">aa_biochemical_fts1</span><span class="p">,</span> <span class="n">ProteinAnalysis</span><span class="p">(</span><span class="s1">&#39;FWYA&#39;</span><span class="p">))</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">aa_biochemical_fts1</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="n">get_aa_aromaticity</span><span class="p">(</span><span class="n">aa_biochemical_fts1</span><span class="p">,</span> <span class="n">ProteinAnalysis</span><span class="p">(</span><span class="s1">'FWYA'</span><span class="p">))</span>
 <span class="n">aa_biochemical_fts2</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="n">get_aa_aromaticity</span><span class="p">(</span><span class="n">aa_biochemical_fts2</span><span class="p">,</span> <span class="n">ProteinAnalysis</span><span class="p">(</span><span class="s1">&#39;AAAA&#39;</span><span class="p">))</span>
-<span class="k">assert</span> <span class="n">aa_biochemical_fts1</span><span class="p">[</span><span class="s1">&#39;Aromaticity&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">aa_biochemical_fts2</span><span class="p">[</span><span class="s1">&#39;Aromaticity&#39;</span><span class="p">]</span>
+<span class="n">get_aa_aromaticity</span><span class="p">(</span><span class="n">aa_biochemical_fts2</span><span class="p">,</span> <span class="n">ProteinAnalysis</span><span class="p">(</span><span class="s1">'AAAA'</span><span class="p">))</span>
+<span class="k">assert</span> <span class="n">aa_biochemical_fts1</span><span class="p">[</span><span class="s1">'Aromaticity'</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">aa_biochemical_fts2</span><span class="p">[</span><span class="s1">'Aromaticity'</span><span class="p">]</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 </div>
     {% endraw %}
 
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 <div class="output_wrapper">
 <div class="output">
-
 <div class="output_area">
-
-
-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="featurize_aa_seqs" class="doc_header"><code>featurize_aa_seqs</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L97" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>featurize_aa_seqs</code>(<strong><code>aa_sequences</code></strong>, <strong><code>features</code></strong>=<em><code>None</code></em>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="featurize_aa_seqs"><code>featurize_aa_seqs</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L97" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>featurize_aa_seqs</code>(<strong><code>aa_sequences</code></strong>, <strong><code>features</code></strong>=<em><code>None</code></em>)</p>
 </blockquote>
 <p>Get feature DataFrame for a list of amino acid sequences</p>
 <p>:param aa_sequences: list of str
 :param features: list or None
 :return: DataFrame</p>
-
 </div>
-
 </div>
-
 </div>
 </div>
-
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     {% endraw %}
 
     {% raw %}
     
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-
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     {% endraw %}
 
@@ -428,52 +352,42 @@ <h4 id="featurize_aa_seqs" class="doc_header"><code>featurize_aa_seqs</code><a h
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ft_dict_df</span> <span class="o">=</span> <span class="n">featurize_aa_seqs</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s1">&#39;ACDG*-&#39;</span><span class="p">,</span> <span class="s1">&#39;CDG*--&#39;</span><span class="p">,</span> <span class="s1">&#39;LLLLLL&#39;</span><span class="p">]))</span>
-<span class="k">assert</span> <span class="n">ft_dict_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;LLLLLL&#39;</span><span class="p">,</span> <span class="s1">&#39;Hydrophobicity&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="n">ft_dict_df</span><span class="p">[</span><span class="s1">&#39;Hydrophobicity&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">ft_dict_df</span> <span class="o">=</span> <span class="n">featurize_aa_seqs</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s1">'ACDG*-'</span><span class="p">,</span> <span class="s1">'CDG*--'</span><span class="p">,</span> <span class="s1">'LLLLLL'</span><span class="p">]))</span>
+<span class="k">assert</span> <span class="n">ft_dict_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'LLLLLL'</span><span class="p">,</span> <span class="s1">'Hydrophobicity'</span><span class="p">]</span> <span class="o">==</span> <span class="n">ft_dict_df</span><span class="p">[</span><span class="s1">'Hydrophobicity'</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
 </div>
     {% endraw %}
 
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
-
 <div class="output_wrapper">
 <div class="output">
-
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-
-
-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="extract_amino_acid_subsequence" class="doc_header"><code>extract_amino_acid_subsequence</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L132" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>extract_amino_acid_subsequence</code>(<strong><code>sg_aas</code></strong>, <strong><code>width</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="extract_amino_acid_subsequence"><code>extract_amino_acid_subsequence</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L132" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>extract_amino_acid_subsequence</code>(<strong><code>sg_aas</code></strong>, <strong><code>width</code></strong>)</p>
 </blockquote>
 <p>Get the amino acid subsequence with a width of <code>width</code> on either side of the Amino Acid index</p>
 <p>:param sg_aas: DataFrame, sgRNA designs merged with amino acid sequence
 :param width: int
 :return: DataFrame</p>
-
 </div>
-
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-
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-
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     {% endraw %}
 
     {% raw %}
     
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     {% endraw %}
 
@@ -481,37 +395,32 @@ <h4 id="extract_amino_acid_subsequence" class="doc_header"><code>extract_amino_a
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">small_aa_seq_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;AA Index&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">9</span><span class="p">],</span>
-                                    <span class="s1">&#39;seq&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;MAVLKYSLW&#39;</span><span class="p">]</span><span class="o">*</span><span class="mi">3</span><span class="p">})</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">small_aa_seq_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'AA Index'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">9</span><span class="p">],</span>
+                                    <span class="s1">'seq'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'MAVLKYSLW'</span><span class="p">]</span><span class="o">*</span><span class="mi">3</span><span class="p">})</span>
 <span class="n">small_aa_subseq_df</span> <span class="o">=</span> <span class="n">extract_amino_acid_subsequence</span><span class="p">(</span><span class="n">small_aa_seq_df</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="n">actual_subseqs</span> <span class="o">=</span> <span class="n">small_aa_subseq_df</span><span class="p">[</span><span class="s1">&#39;AA Subsequence&#39;</span><span class="p">]</span>
-<span class="n">expected_subseqs</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;--MAV&#39;</span><span class="p">,</span> <span class="s1">&#39;VLKYS&#39;</span><span class="p">,</span> <span class="s1">&#39;SLW*-&#39;</span><span class="p">]</span>
+<span class="n">actual_subseqs</span> <span class="o">=</span> <span class="n">small_aa_subseq_df</span><span class="p">[</span><span class="s1">'AA Subsequence'</span><span class="p">]</span>
+<span class="n">expected_subseqs</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'--MAV'</span><span class="p">,</span> <span class="s1">'VLKYS'</span><span class="p">,</span> <span class="s1">'SLW*-'</span><span class="p">]</span>
 <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">actual_subseqs</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">expected_subseqs</span><span class="p">)</span>
 <span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">a</span> <span class="o">==</span> <span class="n">b</span> <span class="k">for</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">actual_subseqs</span><span class="p">,</span> <span class="n">expected_subseqs</span><span class="p">)])</span>
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-<h4 id="get_aa_subseq_df" class="doc_header"><code>get_aa_subseq_df</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L155" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_aa_subseq_df</code>(<strong><code>sg_designs</code></strong>, <strong><code>aa_seq_df</code></strong>, <strong><code>width</code></strong>, <strong><code>id_cols</code></strong>, <strong><code>transcript_base_col</code></strong>=<em><code>'Transcript Base'</code></em>, <strong><code>target_transcript_col</code></strong>=<em><code>'Target Transcript'</code></em>, <strong><code>aa_index_col</code></strong>=<em><code>'AA Index'</code></em>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="get_aa_subseq_df"><code>get_aa_subseq_df</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L155" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>get_aa_subseq_df</code>(<strong><code>sg_designs</code></strong>, <strong><code>aa_seq_df</code></strong>, <strong><code>width</code></strong>, <strong><code>id_cols</code></strong>, <strong><code>transcript_base_col</code></strong>=<em><code>'Transcript Base'</code></em>, <strong><code>target_transcript_col</code></strong>=<em><code>'Target Transcript'</code></em>, <strong><code>aa_index_col</code></strong>=<em><code>'AA Index'</code></em>)</p>
 </blockquote>
 <p>Get the amino acid subsequences for a design dataframe</p>
 <p>:param sg_designs: DataFrame
@@ -521,21 +430,16 @@ <h4 id="get_aa_subseq_df" class="doc_header"><code>get_aa_subseq_df</code><a hre
 :param target_transcript_col: str
 :param aa_index_col: str
 :return: DataFrame</p>
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@@ -543,38 +447,36 @@ <h4 id="get_aa_subseq_df" class="doc_header"><code>get_aa_subseq_df</code><a hre
     
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">aa_seq_df</span> <span class="o">=</span> <span class="n">targetdata</span><span class="o">.</span><span class="n">build_transcript_aa_seq_df</span><span class="p">(</span><span class="n">design_targ_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">aa_seq_df</span> <span class="o">=</span> <span class="n">targetdata</span><span class="o">.</span><span class="n">build_transcript_aa_seq_df</span><span class="p">(</span><span class="n">design_targ_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
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 <pre>Getting amino acid sequences
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-<pre>100%|██████████| 4/4 [00:03&lt;00:00,  1.01it/s]
+<pre>  0%|          | 0/4 [00:00&lt;?, ?it/s]</pre>
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+<pre>100%|██████████| 4/4 [00:02&lt;00:00,  1.56it/s]
+/Users/peterdeweirdt/Documents/rs3/rs3/targetdata.py:110: UserWarning: Unable to find translations for the following transcripts: ENST00000629399, ENST00000284049
+  warnings.warn('Unable to find translations for the following transcripts: ' + ', '.join(missing_seqs))
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@@ -582,19 +484,16 @@ <h4 id="get_aa_subseq_df" class="doc_header"><code>get_aa_subseq_df</code><a hre
     
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">aa_subseq_df</span> <span class="o">=</span> <span class="n">get_aa_subseq_df</span><span class="p">(</span><span class="n">sg_designs</span><span class="o">=</span><span class="n">design_targ_df</span><span class="p">,</span> <span class="n">aa_seq_df</span><span class="o">=</span><span class="n">aa_seq_df</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
-                                <span class="n">id_cols</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sgRNA Context Sequence&#39;</span><span class="p">,</span> <span class="s1">&#39;Target Cut Length&#39;</span><span class="p">,</span> <span class="s1">&#39;Target Transcript&#39;</span><span class="p">,</span> <span class="s1">&#39;Orientation&#39;</span><span class="p">])</span>
-<span class="k">assert</span> <span class="p">(</span><span class="n">aa_subseq_df</span><span class="p">[</span><span class="s1">&#39;AA Subsequence&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">len</span><span class="p">()</span> <span class="o">==</span> <span class="mi">33</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
-<span class="k">assert</span> <span class="n">aa_subseq_df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">design_targ_df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">aa_subseq_df</span> <span class="o">=</span> <span class="n">get_aa_subseq_df</span><span class="p">(</span><span class="n">sg_designs</span><span class="o">=</span><span class="n">design_targ_df</span><span class="p">,</span> <span class="n">aa_seq_df</span><span class="o">=</span><span class="n">aa_seq_df</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
+                                <span class="n">id_cols</span><span class="o">=</span><span class="p">[</span><span class="s1">'sgRNA Context Sequence'</span><span class="p">,</span> <span class="s1">'Target Cut Length'</span><span class="p">,</span> <span class="s1">'Target Transcript'</span><span class="p">,</span> <span class="s1">'Orientation'</span><span class="p">])</span>
+<span class="k">assert</span> <span class="p">(</span><span class="n">aa_subseq_df</span><span class="p">[</span><span class="s1">'AA Subsequence'</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">len</span><span class="p">()</span> <span class="o">==</span> <span class="mi">33</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
+<span class="k">assert</span> <span class="nb">abs</span><span class="p">(</span><span class="n">aa_subseq_df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">design_targ_df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">&lt;</span> <span class="mi">10</span>
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@@ -602,76 +501,66 @@ <h4 id="get_aa_subseq_df" class="doc_header"><code>get_aa_subseq_df</code><a hre
     
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">codon_map_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;test_data/codon_map.csv&#39;</span><span class="p">)</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">codon_map_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'test_data/codon_map.csv'</span><span class="p">)</span>
 
-<span class="k">def</span> <span class="nf">get_rev_comp</span><span class="p">(</span><span class="n">sgrna</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Get reverse compliment of a guide&quot;&quot;&quot;</span>
-    <span class="n">nt_map</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;A&#39;</span><span class="p">:</span> <span class="s1">&#39;T&#39;</span><span class="p">,</span> <span class="s1">&#39;T&#39;</span><span class="p">:</span> <span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;G&#39;</span><span class="p">:</span> <span class="s1">&#39;C&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">:</span> <span class="s1">&#39;G&#39;</span><span class="p">}</span>
-    <span class="n">rev_comp</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
+<span class="k">def</span><span class="w"> </span><span class="nf">get_rev_comp</span><span class="p">(</span><span class="n">sgrna</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">"""Get reverse compliment of a guide"""</span>
+    <span class="n">nt_map</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'A'</span><span class="p">:</span> <span class="s1">'T'</span><span class="p">,</span> <span class="s1">'T'</span><span class="p">:</span> <span class="s1">'A'</span><span class="p">,</span> <span class="s1">'G'</span><span class="p">:</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">:</span> <span class="s1">'G'</span><span class="p">}</span>
+    <span class="n">rev_comp</span> <span class="o">=</span> <span class="s1">''</span>
     <span class="k">for</span> <span class="n">nt</span> <span class="ow">in</span> <span class="n">sgrna</span><span class="p">:</span>
         <span class="n">rev_comp</span> <span class="o">+=</span> <span class="n">nt_map</span><span class="p">[</span><span class="n">nt</span><span class="p">]</span>
     <span class="n">rev_comp</span> <span class="o">=</span> <span class="n">rev_comp</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
     <span class="k">return</span> <span class="n">rev_comp</span>
 
-<span class="n">codon_map</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">codon_map_df</span><span class="p">[</span><span class="s1">&#39;Amino Acid&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">codon_map_df</span><span class="p">[</span><span class="s1">&#39;Codon&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()</span>
+<span class="n">codon_map</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">codon_map_df</span><span class="p">[</span><span class="s1">'Amino Acid'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">codon_map_df</span><span class="p">[</span><span class="s1">'Codon'</span><span class="p">])</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()</span>
 <span class="n">row</span> <span class="o">=</span> <span class="n">aa_subseq_df</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:]</span>
-<span class="n">subseq</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">&#39;AA Subsequence&#39;</span><span class="p">]</span>
-<span class="n">context</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">&#39;sgRNA Context Sequence&#39;</span><span class="p">]</span>
+<span class="n">subseq</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'AA Subsequence'</span><span class="p">]</span>
+<span class="n">context</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'sgRNA Context Sequence'</span><span class="p">]</span>
 <span class="n">rc_context</span> <span class="o">=</span> <span class="n">get_rev_comp</span><span class="p">(</span><span class="n">context</span><span class="p">)</span>
 <span class="n">translations</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
 <span class="n">rc_translations</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
 <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]:</span>
-    <span class="n">translations</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">codon_map</span><span class="p">[</span><span class="n">context</span><span class="p">[</span><span class="n">j</span><span class="p">:</span><span class="n">j</span><span class="o">+</span><span class="mi">3</span><span class="p">]]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">context</span><span class="p">),</span> <span class="mi">3</span><span class="p">)</span>
+    <span class="n">translations</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="s1">''</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">codon_map</span><span class="p">[</span><span class="n">context</span><span class="p">[</span><span class="n">j</span><span class="p">:</span><span class="n">j</span><span class="o">+</span><span class="mi">3</span><span class="p">]]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">context</span><span class="p">),</span> <span class="mi">3</span><span class="p">)</span>
                                <span class="k">if</span> <span class="p">(</span><span class="n">j</span> <span class="o">+</span> <span class="mi">3</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="nb">len</span><span class="p">(</span><span class="n">context</span><span class="p">)])</span>
-    <span class="n">rc_translations</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">codon_map</span><span class="p">[</span><span class="n">rc_context</span><span class="p">[</span><span class="n">j</span><span class="p">:</span><span class="n">j</span><span class="o">+</span><span class="mi">3</span><span class="p">]]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">rc_context</span><span class="p">),</span> <span class="mi">3</span><span class="p">)</span>
+    <span class="n">rc_translations</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="s1">''</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">codon_map</span><span class="p">[</span><span class="n">rc_context</span><span class="p">[</span><span class="n">j</span><span class="p">:</span><span class="n">j</span><span class="o">+</span><span class="mi">3</span><span class="p">]]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">rc_context</span><span class="p">),</span> <span class="mi">3</span><span class="p">)</span>
                                   <span class="k">if</span> <span class="p">(</span><span class="n">j</span> <span class="o">+</span> <span class="mi">3</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="nb">len</span><span class="p">(</span><span class="n">rc_context</span><span class="p">)])</span>
 <span class="k">assert</span> <span class="p">((</span><span class="n">translations</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="n">subseq</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">translations</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">in</span> <span class="n">subseq</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">translations</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="ow">in</span> <span class="n">subseq</span><span class="p">)</span> <span class="ow">or</span>
         <span class="p">(</span><span class="n">rc_translations</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="n">subseq</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">rc_translations</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">in</span> <span class="n">subseq</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">rc_translations</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="ow">in</span> <span class="n">subseq</span><span class="p">))</span>
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-<h4 id="get_amino_acid_features" class="doc_header"><code>get_amino_acid_features</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L177" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_amino_acid_features</code>(<strong><code>aa_subseq_df</code></strong>, <strong><code>features</code></strong>, <strong><code>id_cols</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="get_amino_acid_features"><code>get_amino_acid_features</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L177" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>get_amino_acid_features</code>(<strong><code>aa_subseq_df</code></strong>, <strong><code>features</code></strong>, <strong><code>id_cols</code></strong>)</p>
 </blockquote>
 <p>Featurize amino acid sequences</p>
 <p>:param aa_subseq_df: DataFrame
 :param features: list
 :param id_cols: list
 :return: DataFrame</p>
-
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@@ -679,22 +568,19 @@ <h4 id="get_amino_acid_features" class="doc_header"><code>get_amino_acid_feature
     
 <div class="cell border-box-sizing code_cell rendered">
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-
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-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">aa_features</span> <span class="o">=</span> <span class="n">get_amino_acid_features</span><span class="p">(</span><span class="n">aa_subseq_df</span><span class="o">=</span><span class="n">aa_subseq_df</span><span class="p">,</span>
-                                      <span class="n">features</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Pos. Ind. 1mer&#39;</span><span class="p">,</span>
-                                                <span class="s1">&#39;Hydrophobicity&#39;</span><span class="p">,</span> <span class="s1">&#39;Aromaticity&#39;</span><span class="p">,</span>
-                                                <span class="s1">&#39;Isoelectric Point&#39;</span><span class="p">,</span> <span class="s1">&#39;Secondary Structure&#39;</span><span class="p">],</span>
-                                      <span class="n">id_cols</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sgRNA Context Sequence&#39;</span><span class="p">,</span> <span class="s1">&#39;Target Cut Length&#39;</span><span class="p">,</span>
-                                               <span class="s1">&#39;Target Transcript&#39;</span><span class="p">,</span> <span class="s1">&#39;Orientation&#39;</span><span class="p">])</span>
-<span class="k">assert</span> <span class="n">aa_features</span><span class="p">[</span><span class="s1">&#39;L&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">idxmax</span><span class="p">()</span> <span class="o">==</span> <span class="n">aa_features</span><span class="p">[</span><span class="s1">&#39;Hydrophobicity&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">idxmax</span><span class="p">()</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">aa_features</span> <span class="o">=</span> <span class="n">get_amino_acid_features</span><span class="p">(</span><span class="n">aa_subseq_df</span><span class="o">=</span><span class="n">aa_subseq_df</span><span class="p">,</span>
+                                      <span class="n">features</span><span class="o">=</span><span class="p">[</span><span class="s1">'Pos. Ind. 1mer'</span><span class="p">,</span>
+                                                <span class="s1">'Hydrophobicity'</span><span class="p">,</span> <span class="s1">'Aromaticity'</span><span class="p">,</span>
+                                                <span class="s1">'Isoelectric Point'</span><span class="p">,</span> <span class="s1">'Secondary Structure'</span><span class="p">],</span>
+                                      <span class="n">id_cols</span><span class="o">=</span><span class="p">[</span><span class="s1">'sgRNA Context Sequence'</span><span class="p">,</span> <span class="s1">'Target Cut Length'</span><span class="p">,</span>
+                                               <span class="s1">'Target Transcript'</span><span class="p">,</span> <span class="s1">'Orientation'</span><span class="p">])</span>
+<span class="k">assert</span> <span class="n">aa_features</span><span class="p">[</span><span class="s1">'L'</span><span class="p">]</span><span class="o">.</span><span class="n">idxmax</span><span class="p">()</span> <span class="o">==</span> <span class="n">aa_features</span><span class="p">[</span><span class="s1">'Hydrophobicity'</span><span class="p">]</span><span class="o">.</span><span class="n">idxmax</span><span class="p">()</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
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     {% endraw %}
 
@@ -707,15 +593,13 @@ <h2 id="Protein-Domain-Features">Protein Domain Features<a class="anchor-link" h
     {% raw %}
     
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-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="get_protein_domain_features" class="doc_header"><code>get_protein_domain_features</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L202" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_protein_domain_features</code>(<strong><code>sg_design_df</code></strong>, <strong><code>protein_domains</code></strong>, <strong><code>id_cols</code></strong>, <strong><code>sources</code></strong>=<em><code>None</code></em>, <strong><code>transcript_base_col</code></strong>=<em><code>'Transcript Base'</code></em>, <strong><code>aa_index_col</code></strong>=<em><code>'AA Index'</code></em>, <strong><code>domain_type_col</code></strong>=<em><code>'type'</code></em>, <strong><code>domain_start_col</code></strong>=<em><code>'start'</code></em>, <strong><code>domain_end_col</code></strong>=<em><code>'end'</code></em>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="get_protein_domain_features"><code>get_protein_domain_features</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L202" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>get_protein_domain_features</code>(<strong><code>sg_design_df</code></strong>, <strong><code>protein_domains</code></strong>, <strong><code>id_cols</code></strong>, <strong><code>sources</code></strong>=<em><code>None</code></em>, <strong><code>transcript_base_col</code></strong>=<em><code>'Transcript Base'</code></em>, <strong><code>aa_index_col</code></strong>=<em><code>'AA Index'</code></em>, <strong><code>domain_type_col</code></strong>=<em><code>'type'</code></em>, <strong><code>domain_start_col</code></strong>=<em><code>'start'</code></em>, <strong><code>domain_end_col</code></strong>=<em><code>'end'</code></em>)</p>
 </blockquote>
 <p>Get binary dataframe of protein domains</p>
 <p>:param sg_design_df: DataFrame, with columns [transcript_base_col, aa_index_col]
@@ -728,21 +612,16 @@ <h4 id="get_protein_domain_features" class="doc_header"><code>get_protein_domain
 :param domain_start_col: str
 :param domain_end_col: str
 :return: DataFrame, with binary features for protein domains</p>
-
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-
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@@ -750,41 +629,32 @@ <h4 id="get_protein_domain_features" class="doc_header"><code>get_protein_domain
     
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-
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-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">domain_df</span> <span class="o">=</span> <span class="n">targetdata</span><span class="o">.</span><span class="n">build_translation_overlap_df</span><span class="p">(</span><span class="n">aa_seq_df</span><span class="p">[</span><span class="s1">&#39;id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">(),</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">domain_df</span> <span class="o">=</span> <span class="n">targetdata</span><span class="o">.</span><span class="n">build_translation_overlap_df</span><span class="p">(</span><span class="n">aa_seq_df</span><span class="p">[</span><span class="s1">'id'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">(),</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 <span class="n">protein_domain_feature_df</span> <span class="o">=</span> <span class="n">get_protein_domain_features</span><span class="p">(</span><span class="n">design_targ_df</span><span class="p">,</span> <span class="n">domain_df</span><span class="p">,</span> <span class="n">sources</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                                                        <span class="n">id_cols</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sgRNA Context Sequence&#39;</span><span class="p">,</span> <span class="s1">&#39;Target Cut Length&#39;</span><span class="p">,</span>
-                                                                 <span class="s1">&#39;AA Index&#39;</span><span class="p">,</span> <span class="s1">&#39;Target Transcript&#39;</span><span class="p">,</span> <span class="s1">&#39;Orientation&#39;</span><span class="p">])</span>
+                                                        <span class="n">id_cols</span><span class="o">=</span><span class="p">[</span><span class="s1">'sgRNA Context Sequence'</span><span class="p">,</span> <span class="s1">'Target Cut Length'</span><span class="p">,</span>
+                                                                 <span class="s1">'AA Index'</span><span class="p">,</span> <span class="s1">'Target Transcript'</span><span class="p">,</span> <span class="s1">'Orientation'</span><span class="p">])</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
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 <pre>Getting protein domains
 </pre>
 </div>
 </div>
-
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-
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>100%|██████████| 200/200 [00:49&lt;00:00,  4.04it/s]
+<pre>100%|██████████| 198/198 [00:50&lt;00:00,  3.92it/s]
 </pre>
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-
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-
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     {% endraw %}
 
@@ -792,17 +662,14 @@ <h4 id="get_protein_domain_features" class="doc_header"><code>get_protein_domain
     
 <div class="cell border-box-sizing code_cell rendered">
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-
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-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="n">protein_domain_feature_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">protein_domain_feature_df</span><span class="p">[</span><span class="s1">&#39;sgRNA Context Sequence&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;AAAAGAGCCATGAATCTAAACATCAGGAAT&#39;</span><span class="p">,</span>
-                                     <span class="p">[</span><span class="s1">&#39;PANTHER&#39;</span><span class="p">,</span> <span class="s1">&#39;ncoils&#39;</span><span class="p">,</span> <span class="s1">&#39;Seg&#39;</span><span class="p">,</span> <span class="s1">&#39;MobiDBLite&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">4</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="n">protein_domain_feature_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">protein_domain_feature_df</span><span class="p">[</span><span class="s1">'sgRNA Context Sequence'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'AAAAGAGCCATGAATCTAAACATCAGGAAT'</span><span class="p">,</span>
+                                     <span class="p">[</span><span class="s1">'PANTHER'</span><span class="p">,</span> <span class="s1">'ncoils'</span><span class="p">,</span> <span class="s1">'Seg'</span><span class="p">,</span> <span class="s1">'MobiDBLite'</span><span class="p">]]</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">4</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
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     {% endraw %}
 
@@ -815,39 +682,31 @@ <h2 id="Conservation-Features">Conservation Features<a class="anchor-link" href=
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
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-
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-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="get_conservation_ranges" class="doc_header"><code>get_conservation_ranges</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L247" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_conservation_ranges</code>(<strong><code>cut_pos</code></strong>, <strong><code>small_width</code></strong>, <strong><code>large_width</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="get_conservation_ranges"><code>get_conservation_ranges</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L247" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>get_conservation_ranges</code>(<strong><code>cut_pos</code></strong>, <strong><code>small_width</code></strong>, <strong><code>large_width</code></strong>)</p>
 </blockquote>
-
 </div>
-
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-
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-
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     {% endraw %}
 
     {% raw %}
     
 <div class="cell border-box-sizing code_cell rendered">
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-
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-
-
-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="get_conservation_features" class="doc_header"><code>get_conservation_features</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L253" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_conservation_features</code>(<strong><code>sg_designs</code></strong>, <strong><code>conservation_df</code></strong>, <strong><code>conservation_column</code></strong>, <strong><code>small_width</code></strong>, <strong><code>large_width</code></strong>, <strong><code>id_cols</code></strong>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="get_conservation_features"><code>get_conservation_features</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L253" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>get_conservation_features</code>(<strong><code>sg_designs</code></strong>, <strong><code>conservation_df</code></strong>, <strong><code>conservation_column</code></strong>, <strong><code>small_width</code></strong>, <strong><code>large_width</code></strong>, <strong><code>id_cols</code></strong>)</p>
 </blockquote>
 <p>Get conservation features</p>
 <p>:param sg_designs: DataFrame
@@ -856,21 +715,16 @@ <h4 id="get_conservation_features" class="doc_header"><code>get_conservation_fea
 :param small_width: int, small window length to average scores in one direction
 :param large_width: int, large window length to average scores in the one direction
 :return: DataFrame of conservation features</p>
-
 </div>
-
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-
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-
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     {% endraw %}
 
     {% raw %}
     
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     {% endraw %}
 
@@ -878,88 +732,73 @@ <h4 id="get_conservation_features" class="doc_header"><code>get_conservation_fea
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
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-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">conservation_df</span> <span class="o">=</span> <span class="n">targetdata</span><span class="o">.</span><span class="n">build_conservation_df</span><span class="p">(</span><span class="n">design_targ_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">max_n_jobs</span><span class="p">)</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">conservation_df</span> <span class="o">=</span> <span class="n">targetdata</span><span class="o">.</span><span class="n">build_conservation_df</span><span class="p">(</span><span class="n">design_targ_df</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">max_n_jobs</span><span class="p">)</span>
 <span class="n">conservation_features</span> <span class="o">=</span> <span class="n">get_conservation_features</span><span class="p">(</span><span class="n">design_targ_df</span><span class="p">,</span> <span class="n">conservation_df</span><span class="p">,</span>
                                                   <span class="n">small_width</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">large_width</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
-                                                  <span class="n">conservation_column</span><span class="o">=</span><span class="s1">&#39;ranked_conservation&#39;</span><span class="p">,</span>
-                                                  <span class="n">id_cols</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sgRNA Context Sequence&#39;</span><span class="p">,</span> <span class="s1">&#39;Target Cut Length&#39;</span><span class="p">,</span>
-                                                           <span class="s1">&#39;Target Transcript&#39;</span><span class="p">,</span> <span class="s1">&#39;Orientation&#39;</span><span class="p">])</span>
-<span class="n">merged_features</span> <span class="o">=</span> <span class="n">protein_domain_feature_df</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">conservation_features</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">&#39;inner&#39;</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sgRNA Context Sequence&#39;</span><span class="p">,</span>
-                                                                                          <span class="s1">&#39;Target Cut Length&#39;</span><span class="p">,</span>
-                                                                                          <span class="s1">&#39;Target Transcript&#39;</span><span class="p">,</span>
-                                                                                          <span class="s1">&#39;Orientation&#39;</span><span class="p">])</span>
-<span class="n">smart_avg_cons</span> <span class="o">=</span> <span class="n">merged_features</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">merged_features</span><span class="p">[</span><span class="s1">&#39;Smart&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">bool</span><span class="p">),</span> <span class="s1">&#39;cons_32&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-<span class="n">non_smart_avg_cons</span> <span class="o">=</span> <span class="n">merged_features</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="o">~</span><span class="n">merged_features</span><span class="p">[</span><span class="s1">&#39;Smart&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">bool</span><span class="p">),</span> <span class="s1">&#39;cons_32&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+                                                  <span class="n">conservation_column</span><span class="o">=</span><span class="s1">'ranked_conservation'</span><span class="p">,</span>
+                                                  <span class="n">id_cols</span><span class="o">=</span><span class="p">[</span><span class="s1">'sgRNA Context Sequence'</span><span class="p">,</span> <span class="s1">'Target Cut Length'</span><span class="p">,</span>
+                                                           <span class="s1">'Target Transcript'</span><span class="p">,</span> <span class="s1">'Orientation'</span><span class="p">])</span>
+<span class="n">merged_features</span> <span class="o">=</span> <span class="n">protein_domain_feature_df</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">conservation_features</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">'inner'</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="p">[</span><span class="s1">'sgRNA Context Sequence'</span><span class="p">,</span>
+                                                                                          <span class="s1">'Target Cut Length'</span><span class="p">,</span>
+                                                                                          <span class="s1">'Target Transcript'</span><span class="p">,</span>
+                                                                                          <span class="s1">'Orientation'</span><span class="p">])</span>
+<span class="n">smart_avg_cons</span> <span class="o">=</span> <span class="n">merged_features</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">merged_features</span><span class="p">[</span><span class="s1">'Smart'</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">bool</span><span class="p">),</span> <span class="s1">'cons_32'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+<span class="n">non_smart_avg_cons</span> <span class="o">=</span> <span class="n">merged_features</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="o">~</span><span class="n">merged_features</span><span class="p">[</span><span class="s1">'Smart'</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">bool</span><span class="p">),</span> <span class="s1">'cons_32'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
 <span class="k">assert</span> <span class="n">smart_avg_cons</span> <span class="o">&gt;</span> <span class="n">non_smart_avg_cons</span>
 </pre></div>
-
-    </div>
 </div>
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-
+</div>
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 <pre>Getting conservation
 </pre>
 </div>
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-
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-
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-<pre>100%|██████████| 200/200 [03:35&lt;00:00,  1.08s/it]
+<pre>100%|██████████| 200/200 [04:56&lt;00:00,  1.48s/it]
+/Users/peterdeweirdt/Documents/rs3/rs3/targetdata.py:345: UserWarning: Failed to get conservation scores for 2 transcriptsENST00000629399, ENST00000284049
+  warnings.warn('Failed to get conservation scores for ' + str(len(failed_list)) +
 </pre>
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 <h2 id="Combining-target-features">Combining target features<a class="anchor-link" href="#Combining-target-features"> </a></h2><p>We'll combine, the position, amino acid and domain feature matrices into a single target feature matrix</p>
-
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-<div class="output_markdown rendered_html output_subarea ">
-<h4 id="merge_feature_dfs" class="doc_header"><code>merge_feature_dfs</code><a href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L292" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>merge_feature_dfs</code>(<strong><code>design_df</code></strong>, <strong><code>aa_subseq_df</code></strong>, <strong><code>aa_features</code></strong>=<em><code>None</code></em>, <strong><code>domain_df</code></strong>=<em><code>None</code></em>, <strong><code>conservation_df</code></strong>=<em><code>None</code></em>, <strong><code>id_cols</code></strong>=<em><code>None</code></em>)</p>
+<div class="output_markdown rendered_html output_subarea">
+<h4 class="doc_header" id="merge_feature_dfs"><code>merge_feature_dfs</code><a class="source_link" href="https://github.com/gpp-rnd/rs3/tree/master/rs3/targetfeat.py#L292" style="float:right">[source]</a></h4>
+<blockquote>
+<p><code>merge_feature_dfs</code>(<strong><code>design_df</code></strong>, <strong><code>aa_subseq_df</code></strong>, <strong><code>aa_features</code></strong>=<em><code>None</code></em>, <strong><code>domain_df</code></strong>=<em><code>None</code></em>, <strong><code>conservation_df</code></strong>=<em><code>None</code></em>, <strong><code>id_cols</code></strong>=<em><code>None</code></em>)</p>
 </blockquote>
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     {% endraw %}
 
     {% raw %}
     
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@@ -967,19 +806,16 @@ <h4 id="merge_feature_dfs" class="doc_header"><code>merge_feature_dfs</code><a h
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">feature_df</span><span class="p">,</span> <span class="n">feature_list</span> <span class="o">=</span> <span class="n">merge_feature_dfs</span><span class="p">(</span><span class="n">design_df</span><span class="o">=</span><span class="n">design_df</span><span class="p">,</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">feature_df</span><span class="p">,</span> <span class="n">feature_list</span> <span class="o">=</span> <span class="n">merge_feature_dfs</span><span class="p">(</span><span class="n">design_df</span><span class="o">=</span><span class="n">design_df</span><span class="p">,</span>
                                              <span class="n">aa_subseq_df</span><span class="o">=</span><span class="n">aa_subseq_df</span><span class="p">,</span>
                                              <span class="n">domain_df</span><span class="o">=</span><span class="n">protein_domain_feature_df</span><span class="p">,</span>
                                              <span class="n">conservation_df</span><span class="o">=</span><span class="n">conservation_features</span><span class="p">)</span>
 </pre></div>
-
-    </div>
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-
+</div>
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     {% endraw %}
 
@@ -987,19 +823,14 @@ <h4 id="merge_feature_dfs" class="doc_header"><code>merge_feature_dfs</code><a h
     
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="n">feature_df</span><span class="p">[</span><span class="n">feature_list</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">feature_list</span><span class="p">)</span>
+<div class="input_area">
+<div class="highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="n">feature_df</span><span class="p">[</span><span class="n">feature_list</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">feature_list</span><span class="p">)</span>
 </pre></div>
-
-    </div>
 </div>
 </div>
-
+</div>
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     {% endraw %}
 
 </div>
- 
-
diff --git a/index.ipynb b/index.ipynb
index 7ef32cf..51eb6bd 100644
--- a/index.ipynb
+++ b/index.ipynb
@@ -113,7 +113,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 2/2 [00:00<00:00, 15.04it/s]\n"
+      "100%|██████████| 2/2 [00:00<00:00, 352.28it/s]\n"
      ]
     },
     {
@@ -864,9 +864,9 @@
        "      <th>id</th>\n",
        "      <th>AA len</th>\n",
        "      <th>Target Cut Length</th>\n",
+       "      <th>AA Index</th>\n",
        "      <th>Orientation</th>\n",
        "      <th>sgRNA Context Sequence</th>\n",
-       "      <th>AA Index</th>\n",
        "      <th>extended_seq</th>\n",
        "      <th>AA 0-Indexed</th>\n",
        "      <th>AA 0-Indexed padded</th>\n",
@@ -888,9 +888,9 @@
        "      <td>ENSP00000259457</td>\n",
        "      <td>277</td>\n",
        "      <td>191</td>\n",
+       "      <td>64</td>\n",
        "      <td>sense</td>\n",
        "      <td>TGGAGCAGATACAAGAGCAACTGAAGGGAT</td>\n",
-       "      <td>64</td>\n",
        "      <td>-----------------MAAVSVYAPPVGGFSFDNCRRNAVLEADF...</td>\n",
        "      <td>63</td>\n",
        "      <td>80</td>\n",
@@ -910,9 +910,9 @@
        "      <td>ENSP00000259457</td>\n",
        "      <td>277</td>\n",
        "      <td>137</td>\n",
+       "      <td>46</td>\n",
        "      <td>sense</td>\n",
        "      <td>CCGGAAAACTGGCACGACCATCGCTGGGGT</td>\n",
-       "      <td>46</td>\n",
        "      <td>-----------------MAAVSVYAPPVGGFSFDNCRRNAVLEADF...</td>\n",
        "      <td>45</td>\n",
        "      <td>62</td>\n",
@@ -932,9 +932,9 @@
        "      <td>ENSP00000377793</td>\n",
        "      <td>620</td>\n",
        "      <td>316</td>\n",
+       "      <td>106</td>\n",
        "      <td>sense</td>\n",
        "      <td>TAGAAAAAGATTTGCGCACCCAAGTGGAAT</td>\n",
-       "      <td>106</td>\n",
        "      <td>-----------------MRRSEVLAEESIVCLQKALNHLREIWELI...</td>\n",
        "      <td>105</td>\n",
        "      <td>122</td>\n",
@@ -954,9 +954,9 @@
        "      <td>ENSP00000377793</td>\n",
        "      <td>620</td>\n",
        "      <td>787</td>\n",
+       "      <td>263</td>\n",
        "      <td>antisense</td>\n",
        "      <td>TGGCCTTTGACCCAGACATAATGGTGGCCA</td>\n",
-       "      <td>263</td>\n",
        "      <td>-----------------MRRSEVLAEESIVCLQKALNHLREIWELI...</td>\n",
        "      <td>262</td>\n",
        "      <td>279</td>\n",
@@ -976,9 +976,9 @@
        "      <td>ENSP00000354522</td>\n",
        "      <td>765</td>\n",
        "      <td>420</td>\n",
+       "      <td>140</td>\n",
        "      <td>antisense</td>\n",
        "      <td>AAATACTCACTCATCCTCATCTCGAGGTCT</td>\n",
-       "      <td>140</td>\n",
        "      <td>-----------------MSGDHLHNDSQIEADFRLNDSHKHKDKHK...</td>\n",
        "      <td>139</td>\n",
        "      <td>156</td>\n",
@@ -1005,19 +1005,19 @@
        "3  MRRSEVLAEESIVCLQKALNHLREIWELIGIPEDQRLQRTEVVKKH...  protein  None   \n",
        "4  MSGDHLHNDSQIEADFRLNDSHKHKDKHKDREHRHKEHKKEKDREK...  protein  None   \n",
        "\n",
-       "                id  AA len  Target Cut Length Orientation  \\\n",
-       "0  ENSP00000259457     277                191       sense   \n",
-       "1  ENSP00000259457     277                137       sense   \n",
-       "2  ENSP00000377793     620                316       sense   \n",
-       "3  ENSP00000377793     620                787   antisense   \n",
-       "4  ENSP00000354522     765                420   antisense   \n",
+       "                id  AA len  Target Cut Length  AA Index Orientation  \\\n",
+       "0  ENSP00000259457     277                191        64       sense   \n",
+       "1  ENSP00000259457     277                137        46       sense   \n",
+       "2  ENSP00000377793     620                316       106       sense   \n",
+       "3  ENSP00000377793     620                787       263   antisense   \n",
+       "4  ENSP00000354522     765                420       140   antisense   \n",
        "\n",
-       "           sgRNA Context Sequence  AA Index  \\\n",
-       "0  TGGAGCAGATACAAGAGCAACTGAAGGGAT        64   \n",
-       "1  CCGGAAAACTGGCACGACCATCGCTGGGGT        46   \n",
-       "2  TAGAAAAAGATTTGCGCACCCAAGTGGAAT       106   \n",
-       "3  TGGCCTTTGACCCAGACATAATGGTGGCCA       263   \n",
-       "4  AAATACTCACTCATCCTCATCTCGAGGTCT       140   \n",
+       "           sgRNA Context Sequence  \\\n",
+       "0  TGGAGCAGATACAAGAGCAACTGAAGGGAT   \n",
+       "1  CCGGAAAACTGGCACGACCATCGCTGGGGT   \n",
+       "2  TAGAAAAAGATTTGCGCACCCAAGTGGAAT   \n",
+       "3  TGGCCTTTGACCCAGACATAATGGTGGCCA   \n",
+       "4  AAATACTCACTCATCCTCATCTCGAGGTCT   \n",
        "\n",
        "                                        extended_seq  AA 0-Indexed  \\\n",
        "0  -----------------MAAVSVYAPPVGGFSFDNCRRNAVLEADF...            63   \n",
@@ -1040,7 +1040,6 @@
     }
    ],
    "source": [
-    "\n",
     "aa_subseq_df = get_aa_subseq_df(sg_designs=design_targ_df, aa_seq_df=aa_seq_df, width=16,\n",
     "                                id_cols=id_cols)\n",
     "aa_subseq_df.head()"
@@ -1065,9 +1064,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
+      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
+      "  warnings.warn(\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
+      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
       "  warnings.warn(\n",
-      "/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names\n",
       "  warnings.warn(\n"
      ]
     },
@@ -1138,7 +1141,7 @@
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>Preselected</td>\n",
-       "      <td>0.012467</td>\n",
+       "      <td>-0.088666</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -1162,7 +1165,7 @@
        "      <td>2</td>\n",
        "      <td>0</td>\n",
        "      <td>Preselected</td>\n",
-       "      <td>0.048338</td>\n",
+       "      <td>-0.013821</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -1186,7 +1189,7 @@
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>Preselected</td>\n",
-       "      <td>-0.129234</td>\n",
+       "      <td>0.103036</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -1210,7 +1213,7 @@
        "      <td>2</td>\n",
        "      <td>0</td>\n",
        "      <td>Preselected</td>\n",
-       "      <td>0.061647</td>\n",
+       "      <td>0.160578</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -1234,7 +1237,7 @@
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-0.009100</td>\n",
+       "      <td>0.069597</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1278,11 +1281,11 @@
        "4               BRDN0001486452                                  NaN   \n",
        "\n",
        "   Pick Order Picking Round Picking Notes Target Score Lite  \n",
-       "0           1             0   Preselected          0.012467  \n",
-       "1           2             0   Preselected          0.048338  \n",
-       "2           1             0   Preselected         -0.129234  \n",
-       "3           2             0   Preselected          0.061647  \n",
-       "4           2             1           NaN         -0.009100  \n",
+       "0           1             0   Preselected         -0.088666  \n",
+       "1           2             0   Preselected         -0.013821  \n",
+       "2           1             0   Preselected          0.103036  \n",
+       "3           2             0   Preselected          0.160578  \n",
+       "4           2             1           NaN          0.069597  \n",
        "\n",
        "[5 rows x 61 columns]"
       ]
@@ -2104,9 +2107,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
+      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
       "  warnings.warn(\n",
-      "/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
+      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
+      "  warnings.warn(\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names\n",
       "  warnings.warn(\n"
      ]
     },
@@ -2176,8 +2183,8 @@
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>Preselected</td>\n",
-       "      <td>0.012467</td>\n",
-       "      <td>0.152037</td>\n",
+       "      <td>-0.088666</td>\n",
+       "      <td>0.110229</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -2200,8 +2207,8 @@
        "      <td>2</td>\n",
        "      <td>0</td>\n",
        "      <td>Preselected</td>\n",
-       "      <td>0.048338</td>\n",
-       "      <td>0.064880</td>\n",
+       "      <td>-0.013821</td>\n",
+       "      <td>0.018766</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -2224,8 +2231,8 @@
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>Preselected</td>\n",
-       "      <td>-0.129234</td>\n",
-       "      <td>-0.063012</td>\n",
+       "      <td>0.103036</td>\n",
+       "      <td>0.127005</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -2248,8 +2255,8 @@
        "      <td>2</td>\n",
        "      <td>0</td>\n",
        "      <td>Preselected</td>\n",
-       "      <td>0.061647</td>\n",
-       "      <td>-0.126357</td>\n",
+       "      <td>0.160578</td>\n",
+       "      <td>-0.035591</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -2272,8 +2279,8 @@
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-0.009100</td>\n",
-       "      <td>-0.234410</td>\n",
+       "      <td>0.069597</td>\n",
+       "      <td>-0.154246</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2317,11 +2324,11 @@
        "4                                  NaN           2              1   \n",
        "\n",
        "  Picking Notes Target Score Lite Target Score  \n",
-       "0   Preselected          0.012467     0.152037  \n",
-       "1   Preselected          0.048338     0.064880  \n",
-       "2   Preselected         -0.129234    -0.063012  \n",
-       "3   Preselected          0.061647    -0.126357  \n",
-       "4           NaN         -0.009100    -0.234410  \n",
+       "0   Preselected         -0.088666     0.110229  \n",
+       "1   Preselected         -0.013821     0.018766  \n",
+       "2   Preselected          0.103036     0.127005  \n",
+       "3   Preselected          0.160578    -0.035591  \n",
+       "4           NaN          0.069597    -0.154246  \n",
        "\n",
        "[5 rows x 62 columns]"
       ]
@@ -2412,7 +2419,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 400/400 [00:05<00:00, 68.98it/s] \n"
+      "100%|██████████| 400/400 [00:01<00:00, 226.77it/s]\n"
      ]
     },
     {
@@ -2426,10 +2433,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 400/400 [00:01<00:00, 229.85it/s]\n",
-      "/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
+      "100%|██████████| 400/400 [00:00<00:00, 2734.69it/s]\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
+      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
+      "  warnings.warn(\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
+      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
       "  warnings.warn(\n",
-      "/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names\n",
       "  warnings.warn(\n"
      ]
     },
@@ -2498,9 +2509,9 @@
        "      <td>64</td>\n",
        "      <td>ENST00000259457</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.152037</td>\n",
-       "      <td>0.939676</td>\n",
-       "      <td>0.711381</td>\n",
+       "      <td>0.110229</td>\n",
+       "      <td>0.897869</td>\n",
+       "      <td>0.669574</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -2522,9 +2533,9 @@
        "      <td>46</td>\n",
        "      <td>ENST00000259457</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.064880</td>\n",
-       "      <td>-0.229246</td>\n",
-       "      <td>-0.116557</td>\n",
+       "      <td>0.018766</td>\n",
+       "      <td>-0.275359</td>\n",
+       "      <td>-0.162671</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -2546,9 +2557,9 @@
        "      <td>106</td>\n",
        "      <td>ENST00000394249</td>\n",
        "      <td>False</td>\n",
-       "      <td>-0.063012</td>\n",
-       "      <td>-0.106429</td>\n",
-       "      <td>-0.283446</td>\n",
+       "      <td>0.127005</td>\n",
+       "      <td>0.083588</td>\n",
+       "      <td>-0.093429</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -2570,9 +2581,9 @@
        "      <td>263</td>\n",
        "      <td>ENST00000394249</td>\n",
        "      <td>False</td>\n",
-       "      <td>-0.126357</td>\n",
-       "      <td>0.632899</td>\n",
-       "      <td>0.327112</td>\n",
+       "      <td>-0.035591</td>\n",
+       "      <td>0.723665</td>\n",
+       "      <td>0.417878</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -2594,9 +2605,9 @@
        "      <td>140</td>\n",
        "      <td>ENST00000361337</td>\n",
        "      <td>False</td>\n",
-       "      <td>-0.234410</td>\n",
-       "      <td>0.189591</td>\n",
-       "      <td>-0.431445</td>\n",
+       "      <td>-0.154246</td>\n",
+       "      <td>0.269755</td>\n",
+       "      <td>-0.351282</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2633,25 +2644,25 @@
        "4                           0.424001                            -0.197035   \n",
        "\n",
        "   AA Index  Transcript Base  Missing conservation information Target Score  \\\n",
-       "0        64  ENST00000259457                             False     0.152037   \n",
-       "1        46  ENST00000259457                             False     0.064880   \n",
-       "2       106  ENST00000394249                             False    -0.063012   \n",
-       "3       263  ENST00000394249                             False    -0.126357   \n",
-       "4       140  ENST00000361337                             False    -0.234410   \n",
+       "0        64  ENST00000259457                             False     0.110229   \n",
+       "1        46  ENST00000259457                             False     0.018766   \n",
+       "2       106  ENST00000394249                             False     0.127005   \n",
+       "3       263  ENST00000394249                             False    -0.035591   \n",
+       "4       140  ENST00000361337                             False    -0.154246   \n",
        "\n",
        "  RS3 Sequence (Hsu2013 tracr) + Target Score  \\\n",
-       "0                                    0.939676   \n",
-       "1                                   -0.229246   \n",
-       "2                                   -0.106429   \n",
-       "3                                    0.632899   \n",
-       "4                                    0.189591   \n",
+       "0                                    0.897869   \n",
+       "1                                   -0.275359   \n",
+       "2                                    0.083588   \n",
+       "3                                    0.723665   \n",
+       "4                                    0.269755   \n",
        "\n",
        "  RS3 Sequence (Chen2013 tracr) + Target Score  \n",
-       "0                                     0.711381  \n",
-       "1                                    -0.116557  \n",
-       "2                                    -0.283446  \n",
-       "3                                     0.327112  \n",
-       "4                                    -0.431445  \n",
+       "0                                     0.669574  \n",
+       "1                                    -0.162671  \n",
+       "2                                    -0.093429  \n",
+       "3                                     0.417878  \n",
+       "4                                    -0.351282  \n",
        "\n",
        "[5 rows x 68 columns]"
       ]
@@ -2864,9 +2875,9 @@
        "      <td>46</td>\n",
        "      <td>ENST00000259457</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.064880</td>\n",
-       "      <td>-0.229246</td>\n",
-       "      <td>-0.116557</td>\n",
+       "      <td>0.018766</td>\n",
+       "      <td>-0.275359</td>\n",
+       "      <td>-0.162671</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -2888,9 +2899,9 @@
        "      <td>106</td>\n",
        "      <td>ENST00000394249</td>\n",
        "      <td>False</td>\n",
-       "      <td>-0.063012</td>\n",
-       "      <td>-0.106429</td>\n",
-       "      <td>-0.283446</td>\n",
+       "      <td>0.127005</td>\n",
+       "      <td>0.083588</td>\n",
+       "      <td>-0.093429</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -2912,9 +2923,9 @@
        "      <td>50</td>\n",
        "      <td>ENST00000361337</td>\n",
        "      <td>False</td>\n",
-       "      <td>-0.354708</td>\n",
-       "      <td>-0.648835</td>\n",
-       "      <td>-0.377659</td>\n",
+       "      <td>-0.137369</td>\n",
+       "      <td>-0.431496</td>\n",
+       "      <td>-0.160320</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -2936,9 +2947,9 @@
        "      <td>34</td>\n",
        "      <td>ENST00000368328</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.129285</td>\n",
-       "      <td>-0.538114</td>\n",
-       "      <td>-0.179509</td>\n",
+       "      <td>0.120044</td>\n",
+       "      <td>-0.547355</td>\n",
+       "      <td>-0.188750</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -2960,9 +2971,9 @@
        "      <td>157</td>\n",
        "      <td>ENST00000610426</td>\n",
        "      <td>False</td>\n",
-       "      <td>-0.113577</td>\n",
-       "      <td>-0.515797</td>\n",
-       "      <td>-0.736069</td>\n",
+       "      <td>0.047223</td>\n",
+       "      <td>-0.354996</td>\n",
+       "      <td>-0.575268</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2999,25 +3010,25 @@
        "4                          -0.402220                           -0.622492   \n",
        "\n",
        "  AA Index  Transcript Base Missing conservation information  Target Score  \\\n",
-       "0       46  ENST00000259457                            False      0.064880   \n",
-       "1      106  ENST00000394249                            False     -0.063012   \n",
-       "2       50  ENST00000361337                            False     -0.354708   \n",
-       "3       34  ENST00000368328                            False      0.129285   \n",
-       "4      157  ENST00000610426                            False     -0.113577   \n",
+       "0       46  ENST00000259457                            False      0.018766   \n",
+       "1      106  ENST00000394249                            False      0.127005   \n",
+       "2       50  ENST00000361337                            False     -0.137369   \n",
+       "3       34  ENST00000368328                            False      0.120044   \n",
+       "4      157  ENST00000610426                            False      0.047223   \n",
        "\n",
        "   RS3 Sequence (Hsu2013 tracr) + Target Score  \\\n",
-       "0                                    -0.229246   \n",
-       "1                                    -0.106429   \n",
-       "2                                    -0.648835   \n",
-       "3                                    -0.538114   \n",
-       "4                                    -0.515797   \n",
+       "0                                    -0.275359   \n",
+       "1                                     0.083588   \n",
+       "2                                    -0.431496   \n",
+       "3                                    -0.547355   \n",
+       "4                                    -0.354996   \n",
        "\n",
        "   RS3 Sequence (Chen2013 tracr) + Target Score  \n",
-       "0                                     -0.116557  \n",
-       "1                                     -0.283446  \n",
-       "2                                     -0.377659  \n",
-       "3                                     -0.179509  \n",
-       "4                                     -0.736069  \n",
+       "0                                     -0.162671  \n",
+       "1                                     -0.093429  \n",
+       "2                                     -0.160320  \n",
+       "3                                     -0.188750  \n",
+       "4                                     -0.575268  \n",
        "\n",
        "[5 rows x 69 columns]"
       ]
@@ -3049,14 +3060,12 @@
    "outputs": [
     {
      "data": {
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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 288x288 with 1 Axes>"
+       "<Figure size 400x400 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -3104,7 +3113,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 849/849 [00:06<00:00, 137.86it/s]\n"
+      "100%|██████████| 849/849 [00:00<00:00, 2397.41it/s]\n"
      ]
     },
     {
@@ -3118,7 +3127,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 849/849 [00:02<00:00, 321.44it/s]\n"
+      "100%|██████████| 849/849 [00:00<00:00, 3400.56it/s]\n"
      ]
     },
     {
@@ -3132,7 +3141,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 1/1 [00:00<00:00,  1.77it/s]\n"
+      "100%|██████████| 1/1 [00:00<00:00,  4.01it/s]\n"
      ]
     },
     {
@@ -3146,7 +3155,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 3/3 [00:00<00:00, 899.29it/s]\n"
+      "100%|██████████| 3/3 [00:00<00:00, 2714.76it/s]\n"
      ]
     },
     {
@@ -3160,10 +3169,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 3/3 [00:00<00:00, 10.67it/s]\n",
-      "/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
+      "100%|██████████| 3/3 [00:00<00:00, 29.72it/s]\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
+      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
       "  warnings.warn(\n",
-      "/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
+      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
+      "  warnings.warn(\n",
+      "/Users/peterdeweirdt/miniforge3/envs/test_rs3_v7/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SimpleImputer was fitted without feature names\n",
       "  warnings.warn(\n"
      ]
     },
@@ -3232,9 +3245,9 @@
        "      <td>666</td>\n",
        "      <td>ENST00000309311</td>\n",
        "      <td>False</td>\n",
-       "      <td>-0.115549</td>\n",
-       "      <td>0.792261</td>\n",
-       "      <td>0.654408</td>\n",
+       "      <td>-0.143972</td>\n",
+       "      <td>0.763837</td>\n",
+       "      <td>0.625984</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -3256,9 +3269,9 @@
        "      <td>581</td>\n",
        "      <td>ENST00000309311</td>\n",
        "      <td>False</td>\n",
-       "      <td>-0.017643</td>\n",
-       "      <td>0.154226</td>\n",
-       "      <td>0.022776</td>\n",
+       "      <td>-0.027326</td>\n",
+       "      <td>0.144543</td>\n",
+       "      <td>0.013093</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -3280,9 +3293,9 @@
        "      <td>107</td>\n",
        "      <td>ENST00000309311</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.172910</td>\n",
-       "      <td>1.566422</td>\n",
-       "      <td>0.750642</td>\n",
+       "      <td>-0.002082</td>\n",
+       "      <td>1.391430</td>\n",
+       "      <td>0.575650</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -3304,9 +3317,9 @@
        "      <td>406</td>\n",
        "      <td>ENST00000309311</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.121034</td>\n",
-       "      <td>1.025480</td>\n",
-       "      <td>0.129424</td>\n",
+       "      <td>0.080430</td>\n",
+       "      <td>0.984876</td>\n",
+       "      <td>0.088820</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -3328,9 +3341,9 @@
        "      <td>546</td>\n",
        "      <td>ENST00000309311</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.036041</td>\n",
-       "      <td>0.867128</td>\n",
-       "      <td>0.397635</td>\n",
+       "      <td>0.025749</td>\n",
+       "      <td>0.856836</td>\n",
+       "      <td>0.387343</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -3376,9 +3389,9 @@
        "      <td>52</td>\n",
        "      <td>ENST00000369026</td>\n",
        "      <td>False</td>\n",
-       "      <td>-0.299583</td>\n",
-       "      <td>-1.092501</td>\n",
-       "      <td>-0.963464</td>\n",
+       "      <td>-0.136403</td>\n",
+       "      <td>-0.929321</td>\n",
+       "      <td>-0.800284</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>845</th>\n",
@@ -3424,9 +3437,9 @@
        "      <td>24</td>\n",
        "      <td>ENST00000369026</td>\n",
        "      <td>False</td>\n",
-       "      <td>-0.285485</td>\n",
-       "      <td>-1.386788</td>\n",
-       "      <td>-1.581125</td>\n",
+       "      <td>-0.148665</td>\n",
+       "      <td>-1.249969</td>\n",
+       "      <td>-1.444305</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>847</th>\n",
@@ -3448,9 +3461,9 @@
        "      <td>30</td>\n",
        "      <td>ENST00000369026</td>\n",
        "      <td>False</td>\n",
-       "      <td>-0.312348</td>\n",
-       "      <td>-0.929779</td>\n",
-       "      <td>-0.933784</td>\n",
+       "      <td>-0.145388</td>\n",
+       "      <td>-0.762819</td>\n",
+       "      <td>-0.766825</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>848</th>\n",
@@ -3472,9 +3485,9 @@
        "      <td>30</td>\n",
        "      <td>ENST00000369026</td>\n",
        "      <td>False</td>\n",
-       "      <td>-0.312348</td>\n",
-       "      <td>-0.899159</td>\n",
-       "      <td>-0.976478</td>\n",
+       "      <td>-0.145388</td>\n",
+       "      <td>-0.732200</td>\n",
+       "      <td>-0.809518</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -3548,43 +3561,43 @@
        "848                           -0.586811                            -0.664130   \n",
        "\n",
        "     AA Index  Transcript Base Missing conservation information Target Score  \\\n",
-       "0         666  ENST00000309311                            False    -0.115549   \n",
-       "1         581  ENST00000309311                            False    -0.017643   \n",
-       "2         107  ENST00000309311                            False     0.172910   \n",
-       "3         406  ENST00000309311                            False     0.121034   \n",
-       "4         546  ENST00000309311                            False     0.036041   \n",
+       "0         666  ENST00000309311                            False    -0.143972   \n",
+       "1         581  ENST00000309311                            False    -0.027326   \n",
+       "2         107  ENST00000309311                            False    -0.002082   \n",
+       "3         406  ENST00000309311                            False     0.080430   \n",
+       "4         546  ENST00000309311                            False     0.025749   \n",
        "..        ...              ...                              ...          ...   \n",
-       "844        52  ENST00000369026                            False    -0.299583   \n",
+       "844        52  ENST00000369026                            False    -0.136403   \n",
        "845         5  ENST00000369026                            False    -0.003507   \n",
-       "846        24  ENST00000369026                            False    -0.285485   \n",
-       "847        30  ENST00000369026                            False    -0.312348   \n",
-       "848        30  ENST00000369026                            False    -0.312348   \n",
+       "846        24  ENST00000369026                            False    -0.148665   \n",
+       "847        30  ENST00000369026                            False    -0.145388   \n",
+       "848        30  ENST00000369026                            False    -0.145388   \n",
        "\n",
        "     RS3 Sequence (Hsu2013 tracr) + Target Score  \\\n",
-       "0                                       0.792261   \n",
-       "1                                       0.154226   \n",
-       "2                                       1.566422   \n",
-       "3                                       1.025480   \n",
-       "4                                       0.867128   \n",
+       "0                                       0.763837   \n",
+       "1                                       0.144543   \n",
+       "2                                       1.391430   \n",
+       "3                                       0.984876   \n",
+       "4                                       0.856836   \n",
        "..                                           ...   \n",
-       "844                                    -1.092501   \n",
+       "844                                    -0.929321   \n",
        "845                                    -1.923881   \n",
-       "846                                    -1.386788   \n",
-       "847                                    -0.929779   \n",
-       "848                                    -0.899159   \n",
+       "846                                    -1.249969   \n",
+       "847                                    -0.762819   \n",
+       "848                                    -0.732200   \n",
        "\n",
        "    RS3 Sequence (Chen2013 tracr) + Target Score  \n",
-       "0                                       0.654408  \n",
-       "1                                       0.022776  \n",
-       "2                                       0.750642  \n",
-       "3                                       0.129424  \n",
-       "4                                       0.397635  \n",
+       "0                                       0.625984  \n",
+       "1                                       0.013093  \n",
+       "2                                       0.575650  \n",
+       "3                                       0.088820  \n",
+       "4                                       0.387343  \n",
        "..                                           ...  \n",
-       "844                                    -0.963464  \n",
+       "844                                    -0.800284  \n",
        "845                                    -1.823491  \n",
-       "846                                    -1.581125  \n",
-       "847                                    -0.933784  \n",
-       "848                                    -0.976478  \n",
+       "846                                    -1.444305  \n",
+       "847                                    -0.766825  \n",
+       "848                                    -0.809518  \n",
        "\n",
        "[849 rows x 61 columns]"
       ]
@@ -3602,6 +3615,50 @@
     "scored_designs"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "crispick_designs = pd.read_table('test_data/CRISPick_reference_designs.txt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_designs = (scored_designs\n",
+    "                  .merge(crispick_designs[['sgRNA Cut Position (1-based)',\t'sgRNA Sequence', 'On-Target Efficacy Score']]\n",
+    "                         .rename(columns={'On-Target Efficacy Score': 'CRISPick_score'}), how='inner', \n",
+    "                         on=['sgRNA Cut Position (1-based)',\t'sgRNA Sequence']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplots(figsize=(4,4))\n",
+    "gpplot.add_correlation(data=merged_designs, x='CRISPick_score', y='RS3 Sequence (Chen2013 tracr) + Target Score')\n",
+    "sns.scatterplot(data=merged_designs, x='CRISPick_score', y='RS3 Sequence (Chen2013 tracr) + Target Score')\n",
+    "sns.despine()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -3614,7 +3671,8 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.10 64-bit ('rs3': conda)",
+   "display_name": "test_rs3_v7",
+   "language": "python",
    "name": "python3"
   }
  },
diff --git a/rs3/targetdata.py b/rs3/targetdata.py
index be17d3c..ae279d4 100644
--- a/rs3/targetdata.py
+++ b/rs3/targetdata.py
@@ -16,6 +16,7 @@
 import os
 from scipy import stats
 import multiprocessing
+import io
 
 # Cell
 def ensembl_post(ext, data, headers=None, params=None):
@@ -251,7 +252,7 @@ def get_conservation(chr, start, end, genome):
     }
     results = requests.get(api_url, data=params)
     if results.ok:
-        value_df = (pd.DataFrame([pd.Series(x) for x in pd.read_json(results.content.decode('utf8'))[chrom].values])
+        value_df = (pd.DataFrame([pd.Series(x) for x in pd.read_json(io.StringIO(results.content.decode('utf8')))[chrom].values])
                     .rename(columns={'value': 'conservation'}))
     else:
         raise ValueError(results.reason)
diff --git a/settings.ini b/settings.ini
index f3c9158..621951c 100644
--- a/settings.ini
+++ b/settings.ini
@@ -15,7 +15,7 @@ language = English
 custom_sidebar = False
 license = apache2
 status = 2
-requirements = joblib>=1.0.1 pandas>=1.0.0,<2.0.0 lightgbm>=3.0.0,<=3.3.5 sglearn>=1.2.5 tqdm>=4.61.2 pyarrow>=4.0.1 biopython>=1.78 scikit-learn==0.24.2 requests>=2.25.1 numpy<2.0.0
+requirements = joblib>=1.0.1 pandas>=1.0.0 lightgbm>=3.0.0,<=3.3.5 sglearn>=1.2.5 tqdm>=4.61.2 pyarrow>=4.0.1 biopython>=1.78 scikit-learn>=0.24.2,<=1.0.2 requests>=2.25.1 numpy<=1.26.4
 dev_requirements = gpplot>=0.5.0 seaborn>=0.11.0 scipy>=1.0.1 jupyterlab>=3.0.0 nbdev>=1.1.14,<2.0.0 matplotlib>=3.3.4 tabulate>=0.8.9 jupyter-client<=6.1.12
 nbs_path = .
 doc_path = docs
diff --git a/test_data/CRISPick_reference_designs.txt b/test_data/CRISPick_reference_designs.txt
new file mode 100644
index 0000000..9f9c1ea
--- /dev/null
+++ b/test_data/CRISPick_reference_designs.txt
@@ -0,0 +1,850 @@
+Input	Quota	Target Taxon	Target Gene ID	Target Gene Symbol	Target Transcript	Target Alias	CRISPR Mechanism	Target Domain	Reference Sequence	Strand of Target	PAM Policy	Initial Spacing Requirement	Off-Target Match Ruleset Version	Off-Target Tier Policy	Strand of sgRNA	Orientation	sgRNA Cut Position (1-based)	sgRNA Sequence	sgRNA Context Sequence	PAM Sequence	Exon Number	Target Cut Length	Target Total Length	Target Cut %	Other Target Matches	Aggregate CFD Score	Off-Target CFD100 Hits	Off-Target Tier I CFD100 Hits	On-Target Ruleset	On-Target Efficacy Score	On-Target Rank	Pick Order	Picking Round	Picking Notes
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977506	GCCAGATCATCCCCACAGCA	GGGGGCCAGATCATCCCCACAGCACGGCGC	CGG	13	2172	2577	84.3		0.8636	0	0	RS3seq-Chen2013+RS3target	1.430	1			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982400	GAGGCCAGACCCAAAGCCCA	CGTGGAGGCCAGACCCAAAGCCCACGGTAC	CGG	5	637	2577	24.7		4.1773	1	0	RS3seq-Chen2013+RS3target	1.180	2			Off-target CFD100 matches > 0
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3983284	GAGCTCGTAGAAGAGGGAGA	CCGAGAGCTCGTAGAAGAGGGAGATGGCAC	TGG	3	226	2577	8.8		2.1071	0	0	RS3seq-Chen2013+RS3target	1.174	3	1	1	
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977275	CGTGTTCGAGGAGTCCCAGG	GCCACGTGTTCGAGGAGTCCCAGGTGGCCG	TGG	14	2323	2577	90.1		0.4667	0	0	RS3seq-Chen2013+RS3target	1.082	4			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980940	GATGGTGATCATCTGCAACA	GGTGGATGGTGATCATCTGCAACAAGGCGT	AGG	8	1051	2577	40.8		0.7333	0	0	RS3seq-Chen2013+RS3target	1.032	5	2	1	
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977589	ACTGTGTGAGGAGAACATGC	GCGCACTGTGTGAGGAGAACATGCGGGGTG	GGG	13	2089	2577	81.1		2.4771	0	0	RS3seq-Chen2013+RS3target	1.024	6			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3984180	AGACACGCTTCACTGATACC	GGGGAGACACGCTTCACTGATACCCGGAAG	CGG	2	174	2577	6.8		0.0000	0	0	RS3seq-Chen2013+RS3target	1.020	7			Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980514	ATCACCCAGCTGCTCACCTC	GCACATCACCCAGCTGCTCACCTCTGGATT	TGG	9	1346	2577	52.2		0.7143	0	0	RS3seq-Chen2013+RS3target	0.9795	8	3	1	
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982993	TGCAGACGGAGACAGTGCTG	TGCGTGCAGACGGAGACAGTGCTGCGGCAG	CGG	4	426	2577	16.5		2.5625	1	0	RS3seq-Chen2013+RS3target	0.9783	9			Off-target CFD100 matches > 0
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3976723	GCTGACCTGAGGTCCAACAC	CACCGCTGACCTGAGGTCCAACACGGGCGG	GGG	15	2408	2577	93.4		0.3846	0	0	RS3seq-Chen2013+RS3target	0.9587	10			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3978076	GCACAACCGGCTGTACATGA	ACAAGCACAACCGGCTGTACATGAAGGCGC	AGG	12	1810	2577	70.2		0.0000	0	0	RS3seq-Chen2013+RS3target	0.9364	11			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984236	GATGCCCGCCTTGCACACCA	CGATGATGCCCGCCTTGCACACCAGGGAGT	GGG	2	118	2577	4.6		0.9286	0	0	RS3seq-Chen2013+RS3target	0.9262	12			Outside Target Window: 5-65%; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3981969	AGATGGGGTCCAGGATCAGC	TTGAAGATGGGGTCCAGGATCAGCTGGCAG	TGG	6	875	2577	34.0		0.0000	0	0	RS3seq-Chen2013+RS3target	0.9113	13	4	1	
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977438	ACCTCACCTGGATCTCCACA	GTAGACCTCACCTGGATCTCCACAAGGTAG	AGG	13	2240	2577	86.9		1.2561	0	0	RS3seq-Chen2013+RS3target	0.8696	14			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984272	GGACTTGCCATGGTCCACGT	GCGTGGACTTGCCATGGTCCACGTGGGCGA	GGG	2	82	2577	3.2		0.0000	0	0	RS3seq-Chen2013+RS3target	0.8656	15			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980015	AGGCCCACAATGTTCCCACA	CACGAGGCCCACAATGTTCCCACAAGGCAC	AGG	10	1398	2577	54.2		1.2413	0	0	RS3seq-Chen2013+RS3target	0.8553	16			Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980949	CATCTGCAACAAGGCGTCTC	TGATCATCTGCAACAAGGCGTCTCCGGCAG	CGG	8	1042	2577	40.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.8491	17			BsmBI:CGTCTC; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979333	ACTGGCGCCTCACCTTGATG	GGTCACTGGCGCCTCACCTTGATGGGGATG	GGG	11	1709	2577	66.3		0.4663	0	0	RS3seq-Chen2013+RS3target	0.8459	18			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3984320	AGACCAGATCCGCGCCATCA	CGGTAGACCAGATCCGCGCCATCATGGACA	TGG	2	34	2577	1.3		0.0000	0	0	RS3seq-Chen2013+RS3target	0.8313	19			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3981345	CCAGGCCGCACCTTCAGCAG	GGGCCCAGGCCGCACCTTCAGCAGGGGTTT	GGG	7	1005	2577	39.0		1.3654	0	0	RS3seq-Chen2013+RS3target	0.8288	20			Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982903	AAAGTCTGGTAGAGCTCCTC	CTGGAAAGTCTGGTAGAGCTCCTCGGGCTC	GGG	4	516	2577	20.0		0.0588	0	0	RS3seq-Chen2013+RS3target	0.8172	21	5	1	
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3983218	AATGAGGTTGATGAGGAAGC	AGTCAATGAGGTTGATGAGGAAGCCGGCAC	CGG	3	292	2577	11.3		2.3667	0	0	RS3seq-Chen2013+RS3target	0.7921	22			Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3983145	GCTGCCCTCCGAGTCACCGA	GACTGCTGCCCTCCGAGTCACCGATGGCGC	TGG	3	365	2577	14.2		0.0000	0	0	RS3seq-Chen2013+RS3target	0.7847	23			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977863	GCAGTACCTCAACGAGATCA	GTGTGCAGTACCTCAACGAGATCAAGGACA	AGG	12	2023	2577	78.5		0.0000	0	0	RS3seq-Chen2013+RS3target	0.7842	24			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977531	TGATCTGGCCCCCTCCGCGG	GGGATGATCTGGCCCCCTCCGCGGTGGATG	TGG	13	2147	2577	83.3		0.0000	0	0	RS3seq-Chen2013+RS3target	0.7800	25			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979405	TCGGGAGAGCATATCATCGC	GGAGTCGGGAGAGCATATCATCGCGGGCGC	GGG	11	1637	2577	63.5		0.0000	0	0	RS3seq-Chen2013+RS3target	0.7733	26			Ancestry variant frequency exceeded threshold
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982926	CTCCAGCTGCAGCTCCAGCA	CGGGCTCCAGCTGCAGCTCCAGCAGGGCGC	GGG	4	493	2577	19.1		4.4448	1	0	RS3seq-Chen2013+RS3target	0.7602	27			Off-target CFD100 matches > 0; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979819	GCTGGCCAAGTCCGACCCCA	AGCGGCTGGCCAAGTCCGACCCCATGGTGC	TGG	10	1594	2577	61.9		0.0000	0	0	RS3seq-Chen2013+RS3target	0.7587	28			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977320	GGTGGTCGGTGGCATCTACG	AGCAGGTGGTCGGTGGCATCTACGGGGTTT	GGG	14	2278	2577	88.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.7573	29			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982423	TACCGAGGACAGGATCGATC	ACGGTACCGAGGACAGGATCGATCTGGAAG	TGG	5	614	2577	23.8		0.0000	0	0	RS3seq-Chen2013+RS3target	0.7538	30			Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977836	TGTGGTGGCCGGCTTCCAGT	ACAGTGTGGTGGCCGGCTTCCAGTGGGCCA	GGG	12	2050	2577	79.5		1.4078	0	0	RS3seq-Chen2013+RS3target	0.7466	31			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980551	GAGGTCCTCCTTCTTCCCAG	GGTAGAGGTCCTCCTTCTTCCCAGGGGTAT	GGG	9	1309	2577	50.8		2.8669	0	0	RS3seq-Chen2013+RS3target	0.7437	32			Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977456	CAAGGTAGATGGGCTCCATG	TCCACAAGGTAGATGGGCTCCATGAGGCGT	AGG	13	2222	2577	86.2		0.8471	0	0	RS3seq-Chen2013+RS3target	0.7435	33			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3981976	GTCCAGGATCAGCTGGCAGA	TGGGGTCCAGGATCAGCTGGCAGAAGGTGC	AGG	6	868	2577	33.7		2.4313	1	1	RS3seq-Chen2013+RS3target	0.7391	34			Off-target CFD100 matches > 0; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3983152	TGCGCCATCGGTGACTCGGA	CCAATGCGCCATCGGTGACTCGGAGGGCAG	GGG	3	358	2577	13.9		0.0000	0	0	RS3seq-Chen2013+RS3target	0.7353	35			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979876	GGCCGTGGAGGCCAAGAACC	GAGTGGCCGTGGAGGCCAAGAACCCGGCTG	CGG	10	1537	2577	59.6		0.8667	0	0	RS3seq-Chen2013+RS3target	0.7290	36			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982259	GGACATGATGAAGAAGCTGT	TAGAGGACATGATGAAGAAGCTGTGGGGTG	GGG	5	778	2577	30.2		0.9000	0	0	RS3seq-Chen2013+RS3target	0.7274	37			Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977890	CATCCTCACCGACATCACCA	CCAACATCCTCACCGACATCACCAAGGGTG	AGG	12	1996	2577	77.5		0.0000	0	0	RS3seq-Chen2013+RS3target	0.7147	38			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977507	AGAGGCAGCGCCGTGCTGTG	GCATAGAGGCAGCGCCGTGCTGTGGGGATG	GGG	13	2171	2577	84.2		0.8750	0	0	RS3seq-Chen2013+RS3target	0.7101	39			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3984242	GTCCACGCTGACAGACTCCC	GCAAGTCCACGCTGACAGACTCCCTGGTGT	TGG	2	112	2577	4.3		0.0000	0	0	RS3seq-Chen2013+RS3target	0.7098	40			Outside Target Window: 5-65%; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3981983	ATCAGCTGGCAGAAGGTGCG	CAGGATCAGCTGGCAGAAGGTGCGTGGCAG	TGG	6	861	2577	33.4		0.5917	0	0	RS3seq-Chen2013+RS3target	0.6957	41			Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3984176	ACGCTTCACTGATACCCGGA	AGACACGCTTCACTGATACCCGGAAGGACG	AGG	2	178	2577	6.9		0.0000	0	0	RS3seq-Chen2013+RS3target	0.6810	42			Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3983190	AGGAGAAGTCGACATGCCCG	TCCGAGGAGAAGTCGACATGCCCGGGGGAG	GGG	3	320	2577	12.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.6782	43			Ancestry variant frequency exceeded threshold; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3984268	GTCATCGCCCACGTGGACCA	GTCTGTCATCGCCCACGTGGACCATGGCAA	TGG	2	86	2577	3.3		0.0000	0	0	RS3seq-Chen2013+RS3target	0.6751	44			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982258	GACATGATGAAGAAGCTGTG	AGAGGACATGATGAAGAAGCTGTGGGGTGA	GGG	5	779	2577	30.2		2.9546	1	0	RS3seq-Chen2013+RS3target	0.6613	45			Off-target CFD100 matches > 0; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982004	AGTCAGCCACCAGCCCCGAA	AGCAAGTCAGCCACCAGCCCCGAAGGGAAG	GGG	6	840	2577	32.6		1.2284	0	0	RS3seq-Chen2013+RS3target	0.6498	46			Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3985380	GGCAGGGTTACTCACCATGG	GCGAGGCAGGGTTACTCACCATGGTGGCGG	TGG	1	1	2577	0.0		0.0000	0	0	RS3seq-Chen2013+RS3target	0.6487	47			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984350	GTCTACCGTGAAGTTCACCT	TCTGGTCTACCGTGAAGTTCACCTGGGCAA	GGG	2	4	2577	0.2		0.0000	0	0	RS3seq-Chen2013+RS3target	0.6427	48			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979364	GGAGATCTGCCTGAAGGACC	ACCTGGAGATCTGCCTGAAGGACCTGGAGG	TGG	11	1678	2577	65.1		0.3636	0	0	RS3seq-Chen2013+RS3target	0.6281	49			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979814	TCCGTGCCCACCTGCACCAT	GCACTCCGTGCCCACCTGCACCATGGGGTC	GGG	10	1599	2577	62.0		1.5170	0	0	RS3seq-Chen2013+RS3target	0.6276	50			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977215	ACCGGGCAGGCACTCACCAA	CCACACCGGGCAGGCACTCACCAAAGGACT	AGG	14	2383	2577	92.5		0.8314	0	0	RS3seq-Chen2013+RS3target	0.6156	51			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982346	GAACTTGGCCACATACATCT	CGGCGAACTTGGCCACATACATCTCGGCAA	CGG	5	691	2577	26.8		0.3000	0	0	RS3seq-Chen2013+RS3target	0.6147	52			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980885	CCTCGTACAGGAGCTCGCAG	GGCCCCTCGTACAGGAGCTCGCAGCGGTAC	CGG	8	1106	2577	42.9		0.0000	0	0	RS3seq-Chen2013+RS3target	0.6127	53			Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979815	CCGTGCCCACCTGCACCATG	CACTCCGTGCCCACCTGCACCATGGGGTCG	GGG	10	1598	2577	62.0		2.3357	0	0	RS3seq-Chen2013+RS3target	0.6009	54			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980519	CCAGCTGCTCACCTCTGGAT	TCACCCAGCTGCTCACCTCTGGATTGGCTT	TGG	9	1341	2577	52.0		1.0270	0	0	RS3seq-Chen2013+RS3target	0.5939	55			Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978159	TCTCGCGGTACGAGACGACC	ACCGTCTCGCGGTACGAGACGACCGGGTCA	GGG	12	1727	2577	67.0		0.0000	0	0	RS3seq-Chen2013+RS3target	0.5914	56			BsmBI:5'cgTCTC; BsmBI:GAGACG; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3983196	ATCAACCTCATTGACTCCCC	CCTCATCAACCTCATTGACTCCCCCGGGCA	CGG	3	314	2577	12.2		0.7500	0	0	RS3seq-Chen2013+RS3target	0.5859	57			Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3983173	GCATGTCGACTTCTCCTCGG	CCGGGCATGTCGACTTCTCCTCGGAGGTGA	AGG	3	337	2577	13.1		0.0000	0	0	RS3seq-Chen2013+RS3target	0.5854	58			Ancestry variant frequency exceeded threshold; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980906	GGTACTTCTGGGCCGTCACA	CAGCGGTACTTCTGGGCCGTCACAGGGGAG	GGG	8	1085	2577	42.1		0.0000	0	0	RS3seq-Chen2013+RS3target	0.5752	59			Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3983191	GGAGAAGTCGACATGCCCGG	CCGAGGAGAAGTCGACATGCCCGGGGGAGT	GGG	3	319	2577	12.4		0.7333	0	0	RS3seq-Chen2013+RS3target	0.5587	60			Ancestry variant frequency exceeded threshold; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977824	GCCGTGCCTCACCTCCTTGG	CTGAGCCGTGCCTCACCTCCTTGGTGGCCC	TGG	12	2062	2577	80.0		1.7903	0	0	RS3seq-Chen2013+RS3target	0.5584	61			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977905	GGTGATGTCGGTGAGGATGT	CCTTGGTGATGTCGGTGAGGATGTTGGGGC	TGG	12	1981	2577	76.9		6.2474	0	0	RS3seq-Chen2013+RS3target	0.5528	62			Aggregate CFD score > 4.80; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980922	CACAGGGGAGGGCAGGTGGA	CCGTCACAGGGGAGGGCAGGTGGATGGTGA	TGG	8	1069	2577	41.5		11.6412	2	0	RS3seq-Chen2013+RS3target	0.5503	63			Aggregate CFD score > 4.80; Off-target CFD100 matches > 0; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977889	ATCCTCACCGACATCACCAA	CAACATCCTCACCGACATCACCAAGGGTGT	GGG	12	1997	2577	77.5		0.0000	0	0	RS3seq-Chen2013+RS3target	0.5474	64			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980643	ATGGTGCCAACCTCCGACAA	CAAAATGGTGCCAACCTCCGACAAAGGTCG	AGG	9	1217	2577	47.2		0.0000	0	0	RS3seq-Chen2013+RS3target	0.5461	65			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979424	TCAGTGCATCATCGAGGAGT	CCTTTCAGTGCATCATCGAGGAGTCGGGAG	CGG	11	1618	2577	62.8		0.0000	0	0	RS3seq-Chen2013+RS3target	0.5414	66			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979948	CATGTTGTGCGCGTGCTCGA	CCCGCATGTTGTGCGCGTGCTCGAAGGTGG	AGG	10	1465	2577	56.8		0.0000	0	0	RS3seq-Chen2013+RS3target	0.5373	67			Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982977	AGGCTTGATGCGCTCGGCAA	GCACAGGCTTGATGCGCTCGGCAATGGCCT	TGG	4	442	2577	17.2		0.0000	0	0	RS3seq-Chen2013+RS3target	0.5360	68			Ancestry variant frequency exceeded threshold; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979888	TGTTGTCAGAGTGGCCGTGG	GCCCTGTTGTCAGAGTGGCCGTGGAGGCCA	AGG	10	1525	2577	59.2		0.0500	0	0	RS3seq-Chen2013+RS3target	0.5346	69			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3983189	GAGGAGAAGTCGACATGCCC	CTCCGAGGAGAAGTCGACATGCCCGGGGGA	GGG	3	321	2577	12.5		0.0000	0	0	RS3seq-Chen2013+RS3target	0.5280	70			Ancestry variant frequency exceeded threshold; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3983176	CGGGCATGTCGACTTCTCCT	CCCCCGGGCATGTCGACTTCTCCTCGGAGG	CGG	3	334	2577	13.0		0.0000	0	0	RS3seq-Chen2013+RS3target	0.5264	71			Ancestry variant frequency exceeded threshold; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977821	TGAGCCGTGCCTCACCTCCT	ATGCTGAGCCGTGCCTCACCTCCTTGGTGG	TGG	12	2065	2577	80.1		0.0000	0	0	RS3seq-Chen2013+RS3target	0.5222	72			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984317	GTTGGCCTTCTTGTCCATGA	GGATGTTGGCCTTCTTGTCCATGATGGCGC	TGG	2	37	2577	1.4		0.8409	0	0	RS3seq-Chen2013+RS3target	0.5164	73			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3981344	AGAAGGCAAACCCCTGCTGA	ACAAAGAAGGCAAACCCCTGCTGAAGGTGC	AGG	7	1006	2577	39.0		0.0694	0	0	RS3seq-Chen2013+RS3target	0.5063	74			Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984197	ATCAGTGAAGCGTGTCTCCC	GGGTATCAGTGAAGCGTGTCTCCCCGGCCC	CGG	2	157	2577	6.1		0.0000	0	0	RS3seq-Chen2013+RS3target	0.5043	75			Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980550	AGAGGTCCTCCTTCTTCCCA	AGGTAGAGGTCCTCCTTCTTCCCAGGGGTA	GGG	9	1310	2577	50.8		4.2664	1	0	RS3seq-Chen2013+RS3target	0.5027	76			Off-target CFD100 matches > 0; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977968	CCTGGCCGAGAAGTACGAGT	GCTACCTGGCCGAGAAGTACGAGTGGGACG	GGG	12	1918	2577	74.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4989	77			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976736	CGTGTTGGACCTCAGGTCAG	CGCCCGTGTTGGACCTCAGGTCAGCGGTGA	CGG	15	2395	2577	92.9		0.1364	0	0	RS3seq-Chen2013+RS3target	0.4980	78			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976699	AGTGGTCAAACACACACTGG	TGCCAGTGGTCAAACACACACTGGGGGAAC	GGG	15	2432	2577	94.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4914	79			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977446	TGGATCTCCACAAGGTAGAT	CACCTGGATCTCCACAAGGTAGATGGGCTC	GGG	13	2232	2577	86.6		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4906	80			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980555	TGGGGCCCAACTATACCCCT	ATCATGGGGCCCAACTATACCCCTGGGAAG	GGG	9	1305	2577	50.6		0.1765	0	0	RS3seq-Chen2013+RS3target	0.4897	81			Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980519	TCTACCTGAAGCCAATCCAG	GACCTCTACCTGAAGCCAATCCAGAGGTGA	AGG	9	1341	2577	52.0		1.1382	0	0	RS3seq-Chen2013+RS3target	0.4856	82			Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977531	GCCGACGCCATCCACCGCGG	GCACGCCGACGCCATCCACCGCGGAGGGGG	AGG	13	2147	2577	83.3		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4854	83			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977257	GGCCTTGACCACAAACATGG	GATAGGCCTTGACCACAAACATGGGGGTGC	GGG	14	2341	2577	90.8		0.5333	0	0	RS3seq-Chen2013+RS3target	0.4822	84			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977331	TGTCCAGAGCAGGTGGTCGG	GCAGTGTCCAGAGCAGGTGGTCGGTGGCAT	TGG	14	2267	2577	88.0		0.5333	0	0	RS3seq-Chen2013+RS3target	0.4759	85			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977851	CGAGATCAAGGACAGTGTGG	TCAACGAGATCAAGGACAGTGTGGTGGCCG	TGG	12	2035	2577	79.0		6.9015	0	0	RS3seq-Chen2013+RS3target	0.4713	86			Aggregate CFD score > 4.80; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980895	GAGCTCGCAGCGGTACTTCT	ACAGGAGCTCGCAGCGGTACTTCTGGGCCG	GGG	8	1096	2577	42.5		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4657	87			Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980573	GCCTGAAGGTCAGGATCATG	ACTGGCCTGAAGGTCAGGATCATGGGGCCC	GGG	9	1287	2577	49.9		1.8642	0	0	RS3seq-Chen2013+RS3target	0.4655	88			Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977256	AGGCCTTGACCACAAACATG	AGATAGGCCTTGACCACAAACATGGGGGTG	GGG	14	2342	2577	90.9		0.6250	0	0	RS3seq-Chen2013+RS3target	0.4583	89			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977528	GGATGATCTGGCCCCCTCCG	GTGGGGATGATCTGGCCCCCTCCGCGGTGG	CGG	13	2150	2577	83.4		1.4857	0	0	RS3seq-Chen2013+RS3target	0.4576	90			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3983195	TCAACCTCATTGACTCCCCC	CTCATCAACCTCATTGACTCCCCCGGGCAT	GGG	3	315	2577	12.2		0.3000	0	0	RS3seq-Chen2013+RS3target	0.4549	91			Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978096	ACAGCCGGTTGTGCTTGTTG	ATGTACAGCCGGTTGTGCTTGTTGGGGGAC	GGG	12	1790	2577	69.5		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4493	92			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979851	GACCTGCCCAAGCTGGTGGA	GGCTGACCTGCCCAAGCTGGTGGAGGGGCT	GGG	10	1562	2577	60.6		0.1176	0	0	RS3seq-Chen2013+RS3target	0.4454	93			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3981952	GATCCTGGACCCCATCTTCA	AGCTGATCCTGGACCCCATCTTCAAGGTGA	AGG	6	892	2577	34.6		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4448	94			Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979974	TGATGGTGCCCGTCTTCACC	GTGGTGATGGTGCCCGTCTTCACCAGGAAC	AGG	10	1439	2577	55.8		0.3000	0	0	RS3seq-Chen2013+RS3target	0.4447	95			Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979370	GCACCTGGAGATCTGCCTGA	AGCTGCACCTGGAGATCTGCCTGAAGGACC	AGG	11	1672	2577	64.9		5.9389	1	0	RS3seq-Chen2013+RS3target	0.4434	96			Aggregate CFD score > 4.80; Off-target CFD100 matches > 0
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977481	TGGCTGGGCGGTCAGCACAC	GGCGTGGCTGGGCGGTCAGCACACTGGCAT	TGG	13	2197	2577	85.3		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4424	97			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977999	CCCGTCAGGAGCTCAAGCAG	TCCGCCCGTCAGGAGCTCAAGCAGCGGGCG	CGG	12	1887	2577	73.2		0.0390	0	0	RS3seq-Chen2013+RS3target	0.4340	98			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982383	GCTTTGGGTCTGGCCTCCAC	GTGGGCTTTGGGTCTGGCCTCCACGGGTGG	GGG	5	654	2577	25.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4323	99			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3981361	AGCGAGGACAAGGACAAAGA	GGACAGCGAGGACAAGGACAAAGAAGGCAA	AGG	7	989	2577	38.4		1.8894	0	0	RS3seq-Chen2013+RS3target	0.4303	100			Spacing Violation: Too close to earlier picks at positions 875, 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3983019	CGTCTGCACGCACACGCCTG	TCTCCGTCTGCACGCACACGCCTGGGGACA	GGG	4	400	2577	15.5		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4287	101			Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982379	TGGGTCTGGCCTCCACGGGT	GCTTTGGGTCTGGCCTCCACGGGTGGGCCT	GGG	5	658	2577	25.5		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4272	102			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3978057	AAGGCGCGGCCCTTCCCCGA	CATGAAGGCGCGGCCCTTCCCCGACGGCCT	CGG	12	1829	2577	71.0		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4264	103			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976698	CAGTGGTCAAACACACACTG	CTGCCAGTGGTCAAACACACACTGGGGGAA	GGG	15	2433	2577	94.4		1.6818	1	0	RS3seq-Chen2013+RS3target	0.4252	104			Off-target CFD100 matches > 0; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3981344	CCCAGGCCGCACCTTCAGCA	TGGGCCCAGGCCGCACCTTCAGCAGGGGTT	GGG	7	1006	2577	39.0		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4183	105			Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3984167	TGATACCCGGAAGGACGAGC	TCACTGATACCCGGAAGGACGAGCAGGAGC	AGG	2	187	2577	7.3		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4143	106			Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979378	GGTCCTTCAGGCAGATCTCC	TCCAGGTCCTTCAGGCAGATCTCCAGGTGC	AGG	11	1664	2577	64.6		3.6112	0	0	RS3seq-Chen2013+RS3target	0.4139	107			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980907	GTACTTCTGGGCCGTCACAG	AGCGGTACTTCTGGGCCGTCACAGGGGAGG	GGG	8	1084	2577	42.1		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4096	108			Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3984344	TCTTGCCCAGGTGAACTTCA	CTTGTCTTGCCCAGGTGAACTTCACGGTAG	CGG	2	10	2577	0.4		2.4833	0	0	RS3seq-Chen2013+RS3target	0.4020	109			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979809	TCCGACCCCATGGTGCAGGT	CAAGTCCGACCCCATGGTGCAGGTGGGCAC	GGG	10	1604	2577	62.2		0.8750	0	0	RS3seq-Chen2013+RS3target	0.4011	110			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980556	ATGGGGCCCAACTATACCCC	GATCATGGGGCCCAACTATACCCCTGGGAA	TGG	9	1304	2577	50.6		0.0000	0	0	RS3seq-Chen2013+RS3target	0.3987	111			Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977505	ATAGAGGCAGCGCCGTGCTG	TGGCATAGAGGCAGCGCCGTGCTGTGGGGA	TGG	13	2173	2577	84.3		1.8086	0	0	RS3seq-Chen2013+RS3target	0.3926	112			Ancestry variant frequency exceeded threshold; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976696	GCCAGTGGTCAAACACACAC	ATCTGCCAGTGGTCAAACACACACTGGGGG	TGG	15	2435	2577	94.5		0.0000	0	0	RS3seq-Chen2013+RS3target	0.3835	113			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979813	CAAGTCCGACCCCATGGTGC	TGGCCAAGTCCGACCCCATGGTGCAGGTGG	AGG	10	1600	2577	62.1		0.0000	0	0	RS3seq-Chen2013+RS3target	0.3832	114			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979951	GTTGTGCGCGTGCTCGAAGG	GCATGTTGTGCGCGTGCTCGAAGGTGGTGA	TGG	10	1462	2577	56.7		0.0000	0	0	RS3seq-Chen2013+RS3target	0.3829	115			Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977321	AGGTGGTCGGTGGCATCTAC	GAGCAGGTGGTCGGTGGCATCTACGGGGTT	GGG	14	2277	2577	88.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.3822	116			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979406	GTCGGGAGAGCATATCATCG	AGGAGTCGGGAGAGCATATCATCGCGGGCG	CGG	11	1636	2577	63.5		0.0000	0	0	RS3seq-Chen2013+RS3target	0.3809	117			Ancestry variant frequency exceeded threshold
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977819	AGTGGGCCACCAAGGAGGTG	TTCCAGTGGGCCACCAAGGAGGTGAGGCAC	AGG	12	2067	2577	80.2		1.3982	0	0	RS3seq-Chen2013+RS3target	0.3800	118			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982841	ATCATCTCCACCTACGGCGA	CGTCATCATCTCCACCTACGGCGAGGGCGA	GGG	4	578	2577	22.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.3749	119			Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3983134	AGTCACCGATGGCGCATTGG	TCCGAGTCACCGATGGCGCATTGGTGGTGG	TGG	3	376	2577	14.6		0.0000	0	0	RS3seq-Chen2013+RS3target	0.3740	120			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977236	GGACTCGTTGACGGGCAGAT	CAAAGGACTCGTTGACGGGCAGATAGGCCT	AGG	14	2362	2577	91.7		0.0000	0	0	RS3seq-Chen2013+RS3target	0.3714	121			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982847	AACGTCATCATCTCCACCTA	CGTGAACGTCATCATCTCCACCTACGGCGA	CGG	4	572	2577	22.2		0.0000	0	0	RS3seq-Chen2013+RS3target	0.3657	122			Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977609	TCTCCTCACACAGTGCGCCC	ATGTTCTCCTCACACAGTGCGCCCTGGGGG	TGG	13	2069	2577	80.3		0.3333	0	0	RS3seq-Chen2013+RS3target	0.3648	123			BsmBI:5'cgTCTC; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982944	TGTGCTGATGATGAACAAGA	AGCCTGTGCTGATGATGAACAAGATGGACC	TGG	4	475	2577	18.4		4.4616	0	0	RS3seq-Chen2013+RS3target	0.3600	124			Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3983226	AAGCAGAGCAAGGACGGTGC	CATCAAGCAGAGCAAGGACGGTGCCGGCTT	CGG	3	284	2577	11.0		1.0167	0	0	RS3seq-Chen2013+RS3target	0.3588	125			Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980032	CCGCTACGTGGAGCCCATCG	TGGGCCGCTACGTGGAGCCCATCGAGGATG	AGG	10	1381	2577	53.6		0.1765	0	0	RS3seq-Chen2013+RS3target	0.3475	126			Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3978089	AGTCCCCCAACAAGCACAAC	TCCAAGTCCCCCAACAAGCACAACCGGCTG	CGG	12	1797	2577	69.7		0.3000	0	0	RS3seq-Chen2013+RS3target	0.3448	127			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979329	CCTGCATCCCCATCAAGGTG	CACGCCTGCATCCCCATCAAGGTGAGGCGC	AGG	11	1713	2577	66.5		0.7371	0	0	RS3seq-Chen2013+RS3target	0.3444	128			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980018	CCATCGAGGATGTGCCTTGT	GAGCCCATCGAGGATGTGCCTTGTGGGAAC	GGG	10	1395	2577	54.1		0.0000	0	0	RS3seq-Chen2013+RS3target	0.3402	129			Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977339	ATGCCACCGACCACCTGCTC	GTAGATGCCACCGACCACCTGCTCTGGACA	TGG	14	2259	2577	87.7		0.8824	0	0	RS3seq-Chen2013+RS3target	0.3364	130			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980907	CCACCTGCCCTCCCCTGTGA	CCATCCACCTGCCCTCCCCTGTGACGGCCC	CGG	8	1084	2577	42.1		4.5487	1	0	RS3seq-Chen2013+RS3target	0.3340	131			Off-target CFD100 matches > 0; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980841	CCACCGGGACCACGTACCCA	CCGGCCACCGGGACCACGTACCCATGGCAG	TGG	8	1150	2577	44.6		0.0000	0	0	RS3seq-Chen2013+RS3target	0.3295	132			Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980963	CGTCTCCGGCAGGCAGCCAG	AAGGCGTCTCCGGCAGGCAGCCAGCGGCGC	CGG	8	1028	2577	39.9		0.6154	0	0	RS3seq-Chen2013+RS3target	0.3255	133			BsmBI:CGTCTC; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980044	CTTGATGATGGGCCGCTACG	CAATCTTGATGATGGGCCGCTACGTGGAGC	TGG	10	1369	2577	53.1		1.8571	0	0	RS3seq-Chen2013+RS3target	0.3185	134			Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982405	GATCCTGTCCTCGGTACCGT	GATCGATCCTGTCCTCGGTACCGTGGGCTT	GGG	5	632	2577	24.5		0.0000	0	0	RS3seq-Chen2013+RS3target	0.3161	135			Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982343	GCAGTTTGCCGAGATGTATG	TGAAGCAGTTTGCCGAGATGTATGTGGCCA	TGG	5	694	2577	26.9		0.0000	0	0	RS3seq-Chen2013+RS3target	0.3155	136			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976665	CTGTTGTCGAAGGGGTCTCC	GCTGCTGTTGTCGAAGGGGTCTCCGGGCAG	GGG	15	2466	2577	95.7		0.0000	0	0	RS3seq-Chen2013+RS3target	0.3124	137			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980561	TTCTTCCCAGGGGTATAGTT	CTCCTTCTTCCCAGGGGTATAGTTGGGCCC	GGG	9	1299	2577	50.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.3120	138			Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977428	ACCTTGTGGAGATCCAGGTG	ATCTACCTTGTGGAGATCCAGGTGAGGTCT	AGG	13	2250	2577	87.3		0.0390	0	0	RS3seq-Chen2013+RS3target	0.3072	139			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979361	GATCTGCCTGAAGGACCTGG	TGGAGATCTGCCTGAAGGACCTGGAGGAGG	AGG	11	1681	2577	65.2		1.0913	0	0	RS3seq-Chen2013+RS3target	0.3058	140			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3976734	CAGGCTTCACCGCTGACCTG	TCTGCAGGCTTCACCGCTGACCTGAGGTCC	AGG	15	2397	2577	93.0		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2990	141			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977832	TCACCTCCTTGGTGGCCCAC	TGCCTCACCTCCTTGGTGGCCCACTGGAAG	TGG	12	2054	2577	79.7		0.5385	0	0	RS3seq-Chen2013+RS3target	0.2987	142			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977255	TAGGCCTTGACCACAAACAT	CAGATAGGCCTTGACCACAAACATGGGGGT	GGG	14	2343	2577	90.9		0.1333	0	0	RS3seq-Chen2013+RS3target	0.2967	143			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979819	GCCCACCTGCACCATGGGGT	CCGTGCCCACCTGCACCATGGGGTCGGACT	CGG	10	1594	2577	61.9		0.0222	0	0	RS3seq-Chen2013+RS3target	0.2963	144			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980007	GTGCCTTGTGGGAACATTGT	GGATGTGCCTTGTGGGAACATTGTGGGCCT	GGG	10	1406	2577	54.6		0.4765	0	0	RS3seq-Chen2013+RS3target	0.2956	145			Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980980	CAGCGGCGCATCACAGCCTG	CAGCCAGCGGCGCATCACAGCCTGCGGGGG	CGG	8	1011	2577	39.2		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2952	146			Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982971	CAGCACAGGCTTGATGCGCT	TCATCAGCACAGGCTTGATGCGCTCGGCAA	CGG	4	448	2577	17.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2927	147			Ancestry variant frequency exceeded threshold; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976721	GAACGCCTGGCCGCCCGTGT	GGGGGAACGCCTGGCCGCCCGTGTTGGACC	TGG	15	2410	2577	93.5		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2885	148			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979334	CCACGCCTGCATCCCCATCA	AGGACCACGCCTGCATCCCCATCAAGGTGA	AGG	11	1708	2577	66.3		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2865	149			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977884	GTTGAGGTACTGCACACCCT	TCTCGTTGAGGTACTGCACACCCTTGGTGA	TGG	12	2002	2577	77.7		0.2143	0	0	RS3seq-Chen2013+RS3target	0.2860	150			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979902	CCACGGCCACTCTGACAACA	GCCTCCACGGCCACTCTGACAACAGGGCTG	GGG	10	1511	2577	58.6		0.8667	0	0	RS3seq-Chen2013+RS3target	0.2856	151			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977942	TGGCTGAGGCCCGCAAGATC	GACGTGGCTGAGGCCCGCAAGATCTGGTGC	TGG	12	1944	2577	75.4		0.9231	0	0	RS3seq-Chen2013+RS3target	0.2836	152			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982902	GAAAGTCTGGTAGAGCTCCT	GCTGGAAAGTCTGGTAGAGCTCCTCGGGCT	CGG	4	517	2577	20.1		1.7750	0	0	RS3seq-Chen2013+RS3target	0.2800	153			Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3976634	CAGCAGCCGCCCCAGCCAGG	ACAACAGCAGCCGCCCCAGCCAGGTGGTGG	TGG	15	2497	2577	96.9		14.5179	0	0	RS3seq-Chen2013+RS3target	0.2687	154			Aggregate CFD score > 4.80; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980659	GTCGGAGGTTGGCACCATTT	CTTTGTCGGAGGTTGGCACCATTTTGGAAA	TGG	9	1201	2577	46.6		0.0500	0	0	RS3seq-Chen2013+RS3target	0.2681	155			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977611	TCCTCACACAGTGCGCCCTG	GTTCTCCTCACACAGTGCGCCCTGGGGGAG	GGG	13	2067	2577	80.2		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2568	156			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979391	GATCTCCAGGTGCAGCTCGC	GGCAGATCTCCAGGTGCAGCTCGCCGGCGC	CGG	11	1651	2577	64.1		0.3833	0	0	RS3seq-Chen2013+RS3target	0.2562	157			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3983137	CCGAGTCACCGATGGCGCAT	CCCTCCGAGTCACCGATGGCGCATTGGTGG	TGG	3	373	2577	14.5		0.5714	0	0	RS3seq-Chen2013+RS3target	0.2512	158			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977516	GCCGTGCTGTGGGGATGATC	CAGCGCCGTGCTGTGGGGATGATCTGGCCC	TGG	13	2162	2577	83.9		1.0835	0	0	RS3seq-Chen2013+RS3target	0.2507	159			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977442	CATGGAGCCCATCTACCTTG	GCCTCATGGAGCCCATCTACCTTGTGGAGA	TGG	13	2236	2577	86.8		0.4286	0	0	RS3seq-Chen2013+RS3target	0.2450	160			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980620	CCCCGAGAAGACTCGTCCAA	CCAGCCCCGAGAAGACTCGTCCAAAGGCGT	AGG	9	1240	2577	48.1		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2406	161			Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977433	CATCTACCTTGTGGAGATCC	AGCCCATCTACCTTGTGGAGATCCAGGTGA	AGG	13	2245	2577	87.1		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2394	162			Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3983170	GAGGGCAGCAGTCACCTCCG	CTCGGAGGGCAGCAGTCACCTCCGAGGAGA	AGG	3	340	2577	13.2		0.5000	0	0	RS3seq-Chen2013+RS3target	0.2357	163			Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977338	CTGGCAGTGTCCAGAGCAGG	CCTTCTGGCAGTGTCCAGAGCAGGTGGTCG	TGG	14	2260	2577	87.7		6.2434	0	0	RS3seq-Chen2013+RS3target	0.2341	164			Aggregate CFD score > 4.80; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984173	ACGCTCCTGCTCGTCCTTCC	TGCAACGCTCCTGCTCGTCCTTCCGGGTAT	GGG	2	181	2577	7.0		0.7211	0	0	RS3seq-Chen2013+RS3target	0.2208	165			Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980641	GGCGTAGAACCGACCTTTGT	CAAAGGCGTAGAACCGACCTTTGTCGGAGG	CGG	9	1219	2577	47.3		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2202	166			Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979850	ACCTGCCCAAGCTGGTGGAG	GCTGACCTGCCCAAGCTGGTGGAGGGGCTG	GGG	10	1563	2577	60.7		3.3095	0	0	RS3seq-Chen2013+RS3target	0.2152	167			Quota Met
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980549	TAGAGGTCCTCCTTCTTCCC	CAGGTAGAGGTCCTCCTTCTTCCCAGGGGT	AGG	9	1311	2577	50.9		3.9245	1	0	RS3seq-Chen2013+RS3target	0.2152	168			Off-target CFD100 matches > 0; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980687	TACATCATAAGAGGGCCTTT	AATATACATCATAAGAGGGCCTTTGGGGTC	GGG	9	1173	2577	45.5		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2095	169			Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979981	CGTGGGCGTGGACCAGTTCC	GCCTCGTGGGCGTGGACCAGTTCCTGGTGA	TGG	10	1432	2577	55.6		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2079	170			Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979933	CGAGCACGCGCACAACATGC	CCTTCGAGCACGCGCACAACATGCGGGTGA	GGG	10	1480	2577	57.4		0.8750	0	0	RS3seq-Chen2013+RS3target	0.1967	171			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979885	AGCCGGGTTCTTGGCCTCCA	GGTCAGCCGGGTTCTTGGCCTCCACGGCCA	CGG	10	1528	2577	59.3		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1936	172			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977506	TAGAGGCAGCGCCGTGCTGT	GGCATAGAGGCAGCGCCGTGCTGTGGGGAT	GGG	13	2172	2577	84.3		0.8571	0	0	RS3seq-Chen2013+RS3target	0.1895	173			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3983202	CATGCCCGGGGGAGTCAATG	TCGACATGCCCGGGGGAGTCAATGAGGTTG	AGG	3	308	2577	12.0		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1879	174			Ancestry variant frequency exceeded threshold; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977854	CAACGAGATCAAGGACAGTG	ACCTCAACGAGATCAAGGACAGTGTGGTGG	TGG	12	2032	2577	78.9		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1852	175			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977588	CTGTGTGAGGAGAACATGCG	CGCACTGTGTGAGGAGAACATGCGGGGTGT	GGG	13	2090	2577	81.1		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1804	176			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979813	CTCCGTGCCCACCTGCACCA	TGCACTCCGTGCCCACCTGCACCATGGGGT	TGG	10	1600	2577	62.1		0.4444	0	0	RS3seq-Chen2013+RS3target	0.1802	177			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982842	GCCGCTCTCGCCCTCGCCGT	TGGGGCCGCTCTCGCCCTCGCCGTAGGTGG	AGG	4	577	2577	22.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1802	178			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976697	CCAGTGGTCAAACACACACT	TCTGCCAGTGGTCAAACACACACTGGGGGA	GGG	15	2434	2577	94.5		0.6214	0	0	RS3seq-Chen2013+RS3target	0.1751	179			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3981386	AGAGAAACTGGACATCAAAC	TGATAGAGAAACTGGACATCAAACTGGACA	TGG	7	964	2577	37.4		0.2632	0	0	RS3seq-Chen2013+RS3target	0.1679	180			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 875, 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979357	TGCAGGCGTGGTCCTCCTCC	GGGATGCAGGCGTGGTCCTCCTCCAGGTCC	AGG	11	1685	2577	65.4		4.7322	1	0	RS3seq-Chen2013+RS3target	0.1645	181			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977550	GTGGATGGCGTCGGCGTGCA	CGCGGTGGATGGCGTCGGCGTGCAGGGTGA	GGG	13	2128	2577	82.6		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1571	182			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977254	ATAGGCCTTGACCACAAACA	GCAGATAGGCCTTGACCACAAACATGGGGG	TGG	14	2344	2577	91.0		0.3155	0	0	RS3seq-Chen2013+RS3target	0.1457	183			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3983236	GAACTTCATCAAGCAGAGCA	ACTTGAACTTCATCAAGCAGAGCAAGGACG	AGG	3	274	2577	10.6		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1418	184			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980872	GCTGCGAGCTCCTGTACGAG	TACCGCTGCGAGCTCCTGTACGAGGGGCCC	GGG	8	1119	2577	43.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1402	185			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980587	GCTGGTCTCCACTGGCCTGA	CGGGGCTGGTCTCCACTGGCCTGAAGGTCA	AGG	9	1273	2577	49.4		0.0588	0	0	RS3seq-Chen2013+RS3target	0.1373	186			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979971	GACCAGTTCCTGGTGAAGAC	CGTGGACCAGTTCCTGGTGAAGACGGGCAC	GGG	10	1442	2577	56.0		0.7331	0	0	RS3seq-Chen2013+RS3target	0.1362	187			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3983151	ATGCGCCATCGGTGACTCGG	ACCAATGCGCCATCGGTGACTCGGAGGGCA	AGG	3	359	2577	13.9		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1325	188			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980639	TGCCAACCTCCGACAAAGGT	ATGGTGCCAACCTCCGACAAAGGTCGGTTC	CGG	9	1221	2577	47.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1317	189			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979399	GAGCATATCATCGCGGGCGC	GGGAGAGCATATCATCGCGGGCGCCGGCGA	CGG	11	1643	2577	63.8		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1311	190			Ancestry variant frequency exceeded threshold; On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3984230	AGACTCCCTGGTGTGCAAGG	TGACAGACTCCCTGGTGTGCAAGGCGGGCA	CGG	2	124	2577	4.8		0.5000	0	0	RS3seq-Chen2013+RS3target	0.1271	191			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980846	CCGGACGACGAGGCTGCCAT	GCCCCCGGACGACGAGGCTGCCATGGGTAC	GGG	8	1145	2577	44.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1247	192			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979980	TGCCCGTCTTCACCAGGAAC	ATGGTGCCCGTCTTCACCAGGAACTGGTCC	TGG	10	1433	2577	55.6		1.1697	0	0	RS3seq-Chen2013+RS3target	0.1192	193			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984328	TGTCCATGATGGCGCGGATC	TTCTTGTCCATGATGGCGCGGATCTGGTCT	TGG	2	26	2577	1.0		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1135	194			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980860	ATGGCAGCCTCGTCGTCCGG	ACCCATGGCAGCCTCGTCGTCCGGGGGCCC	GGG	8	1131	2577	43.9		0.0222	0	0	RS3seq-Chen2013+RS3target	0.1126	195			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3984275	CATGTCTGTCATCGCCCACG	GCAACATGTCTGTCATCGCCCACGTGGACC	TGG	2	79	2577	3.1		0.1364	0	0	RS3seq-Chen2013+RS3target	0.1098	196			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977461	TAGATGGGCTCCATGAGGCG	AAGGTAGATGGGCTCCATGAGGCGTGGCTG	TGG	13	2217	2577	86.0		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1089	197			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982051	GTTGGCTGGGTCAAAGTACC	TGCCGTTGGCTGGGTCAAAGTACCTGGCAA	TGG	6	793	2577	30.8		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1086	198			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979385	GGGCGCCGGCGAGCTGCACC	TCGCGGGCGCCGGCGAGCTGCACCTGGAGA	TGG	11	1657	2577	64.3		1.4706	1	0	RS3seq-Chen2013+RS3target	0.1077	199			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977534	CACGCCGACGCCATCCACCG	CCTGCACGCCGACGCCATCCACCGCGGAGG	CGG	13	2144	2577	83.2		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1063	200			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977906	GTGATGTCGGTGAGGATGTT	CTTGGTGATGTCGGTGAGGATGTTGGGGCC	GGG	12	1980	2577	76.8		0.8750	0	0	RS3seq-Chen2013+RS3target	0.1036	201			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982280	CGAGCGGGCCAAGAAAGTAG	CTGCCGAGCGGGCCAAGAAAGTAGAGGACA	AGG	5	757	2577	29.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.09631	202			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982322	CAACTGGCCCTCCCCCTTGG	GCCCCAACTGGCCCTCCCCCTTGGCGGCGA	CGG	5	715	2577	27.7		0.7390	0	0	RS3seq-Chen2013+RS3target	0.09622	203			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978144	ACTCTTCACTGACCGTCTCG	TTCGACTCTTCACTGACCGTCTCGCGGTAC	CGG	12	1742	2577	67.6		0.0000	0	0	RS3seq-Chen2013+RS3target	0.09016	204			BsmBI:CGTCTC; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977839	CTTGGTGGCCCACTGGAAGC	CCTCCTTGGTGGCCCACTGGAAGCCGGCCA	CGG	12	2047	2577	79.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.09015	205			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977334	CAGTGTCCAGAGCAGGTGGT	CTGGCAGTGTCCAGAGCAGGTGGTCGGTGG	CGG	14	2264	2577	87.9		0.0000	0	0	RS3seq-Chen2013+RS3target	0.09007	206			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977228	TCACCAAAGGACTCGTTGAC	GCACTCACCAAAGGACTCGTTGACGGGCAG	GGG	14	2370	2577	92.0		0.0000	0	0	RS3seq-Chen2013+RS3target	0.08967	207			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3981960	TCACCTTGAAGATGGGGTCC	TCACTCACCTTGAAGATGGGGTCCAGGATC	AGG	6	884	2577	34.3		0.0390	0	0	RS3seq-Chen2013+RS3target	0.08647	208			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3981954	CCTCACTCACCTTGAAGATG	CAGCCCTCACTCACCTTGAAGATGGGGTCC	GGG	6	890	2577	34.5		2.1555	0	0	RS3seq-Chen2013+RS3target	0.08639	209			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3983278	CTCCGAGAGCTCGTAGAAGA	CATTCTCCGAGAGCTCGTAGAAGAGGGAGA	GGG	3	232	2577	9.0		0.0000	0	0	RS3seq-Chen2013+RS3target	0.08271	210			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3981952	GCCCTCACTCACCTTGAAGA	CGCAGCCCTCACTCACCTTGAAGATGGGGT	TGG	6	892	2577	34.6		2.0266	0	0	RS3seq-Chen2013+RS3target	0.08220	211			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980915	GGGCCGTCACAGGGGAGGGC	TTCTGGGCCGTCACAGGGGAGGGCAGGTGG	AGG	8	1076	2577	41.8		1.5532	0	0	RS3seq-Chen2013+RS3target	0.07903	212			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978013	CTGCTTGAGCTCCTGACGGG	CCCGCTGCTTGAGCTCCTGACGGGCGGACA	CGG	12	1873	2577	72.7		0.2593	0	0	RS3seq-Chen2013+RS3target	0.07883	213			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978108	GCTTGTTGGGGGACTTGGAG	TTGTGCTTGTTGGGGGACTTGGAGAGGCAG	AGG	12	1778	2577	69.0		2.0568	0	0	RS3seq-Chen2013+RS3target	0.06733	214			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3983140	CACCACCACCAATGCGCCAT	AGTCCACCACCACCAATGCGCCATCGGTGA	CGG	3	370	2577	14.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.06521	215			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980644	GTAGAACCGACCTTTGTCGG	AGGCGTAGAACCGACCTTTGTCGGAGGTTG	AGG	9	1216	2577	47.2		0.0000	0	0	RS3seq-Chen2013+RS3target	0.06182	216			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3984311	CCGCGCCATCATGGACAAGA	AGATCCGCGCCATCATGGACAAGAAGGCCA	AGG	2	43	2577	1.7		0.0000	0	0	RS3seq-Chen2013+RS3target	0.05603	217			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982929	CAAGATGGACCGCGCCCTGC	TGAACAAGATGGACCGCGCCCTGCTGGAGC	TGG	4	490	2577	19.0		0.3889	0	0	RS3seq-Chen2013+RS3target	0.05157	218			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982889	CCACGATGCGCTGGAAAGTC	TTCTCCACGATGCGCTGGAAAGTCTGGTAG	TGG	4	530	2577	20.6		0.0000	0	0	RS3seq-Chen2013+RS3target	0.05152	219			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982824	CGAGGGCGAGAGCGGCCCCA	ACGGCGAGGGCGAGAGCGGCCCCATGGGCA	TGG	4	595	2577	23.1		0.0000	0	0	RS3seq-Chen2013+RS3target	0.05037	220			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980873	CGCTGCGAGCTCCTGTACGA	GTACCGCTGCGAGCTCCTGTACGAGGGGCC	GGG	8	1118	2577	43.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.04241	221			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979934	TCGAGCACGCGCACAACATG	ACCTTCGAGCACGCGCACAACATGCGGGTG	CGG	10	1479	2577	57.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.03971	222			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984349	GGTCTACCGTGAAGTTCACC	ATCTGGTCTACCGTGAAGTTCACCTGGGCA	TGG	2	5	2577	0.2		0.8750	0	0	RS3seq-Chen2013+RS3target	0.03929	223			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982931	GCTGCAGCTCCAGCAGGGCG	TCCAGCTGCAGCTCCAGCAGGGCGCGGTCC	CGG	4	488	2577	18.9		3.6327	0	0	RS3seq-Chen2013+RS3target	0.03727	224			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977907	TGATGTCGGTGAGGATGTTG	TTGGTGATGTCGGTGAGGATGTTGGGGCCG	GGG	12	1979	2577	76.8		2.4464	0	0	RS3seq-Chen2013+RS3target	0.03709	225			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982361	CATCTCGGCAAACTGCTTCA	CATACATCTCGGCAAACTGCTTCAGGGTGA	GGG	5	676	2577	26.2		0.0000	0	0	RS3seq-Chen2013+RS3target	0.03393	226			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3983277	TCTCCGAGAGCTCGTAGAAG	TCATTCTCCGAGAGCTCGTAGAAGAGGGAG	AGG	3	233	2577	9.0		0.0000	0	0	RS3seq-Chen2013+RS3target	0.03153	227			BsmBI:5'cgTCTC; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978010	CCGCTGCTTGAGCTCCTGAC	GCGCCCGCTGCTTGAGCTCCTGACGGGCGG	GGG	12	1876	2577	72.8		0.0000	0	0	RS3seq-Chen2013+RS3target	0.02686	228			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977837	GTGTGGTGGCCGGCTTCCAG	GACAGTGTGGTGGCCGGCTTCCAGTGGGCC	TGG	12	2049	2577	79.5		0.3077	0	0	RS3seq-Chen2013+RS3target	0.02475	229			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3978145	GGTCGTCTCGTACCGCGAGA	ACCCGGTCGTCTCGTACCGCGAGACGGTCA	CGG	12	1741	2577	67.6		0.0000	0	0	RS3seq-Chen2013+RS3target	0.01564	230			BsmBI:CGTCTC; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979856	TTCAGCCCCTCCACCAGCTT	CCGCTTCAGCCCCTCCACCAGCTTGGGCAG	GGG	10	1557	2577	60.4		0.3843	0	0	RS3seq-Chen2013+RS3target	0.01414	231			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979841	AGCTGGTGGAGGGGCTGAAG	CCCAAGCTGGTGGAGGGGCTGAAGCGGCTG	CGG	10	1572	2577	61.0		2.5882	1	0	RS3seq-Chen2013+RS3target	0.01210	232			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978054	TGTCCTCGGCCAGGCCGTCG	TCGATGTCCTCGGCCAGGCCGTCGGGGAAG	GGG	12	1832	2577	71.1		0.0000	0	0	RS3seq-Chen2013+RS3target	0.01107	233			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982989	GACGGAGACAGTGCTGCGGC	TGCAGACGGAGACAGTGCTGCGGCAGGCCA	AGG	4	430	2577	16.7		0.5000	0	0	RS3seq-Chen2013+RS3target	0.005786	234			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979423	CAGTGCATCATCGAGGAGTC	CTTTCAGTGCATCATCGAGGAGTCGGGAGA	GGG	11	1619	2577	62.8		0.0000	0	0	RS3seq-Chen2013+RS3target	0.004703	235			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980605	TGGACGAGTCTTCTCGGGGC	CCTTTGGACGAGTCTTCTCGGGGCTGGTCT	TGG	9	1255	2577	48.7		0.0000	0	0	RS3seq-Chen2013+RS3target	0.003378	236			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977590	CACTGTGTGAGGAGAACATG	GGCGCACTGTGTGAGGAGAACATGCGGGGT	CGG	13	2088	2577	81.0		2.9767	0	0	RS3seq-Chen2013+RS3target	-0.008985	237			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980055	TCCAGAACAATCTTGATGAT	TTCTTCCAGAACAATCTTGATGATGGGCCG	GGG	10	1358	2577	52.7		1.1697	0	0	RS3seq-Chen2013+RS3target	-0.01706	238			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984235	TGATGCCCGCCTTGCACACC	GCGATGATGCCCGCCTTGCACACCAGGGAG	AGG	2	119	2577	4.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.02584	239			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977847	ATCAAGGACAGTGTGGTGGC	CGAGATCAAGGACAGTGTGGTGGCCGGCTT	CGG	12	2039	2577	79.1		3.2605	2	0	RS3seq-Chen2013+RS3target	-0.02630	240			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982000	GCGTGGCAGCTTCTTCCCTT	AGGTGCGTGGCAGCTTCTTCCCTTCGGGGC	CGG	6	844	2577	32.8		0.9474	0	0	RS3seq-Chen2013+RS3target	-0.02642	241			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3976583	GAAAGAAGGCATCCCTGCCC	GCCTGAAAGAAGGCATCCCTGCCCTGGACA	TGG	15	2548	2577	98.9		1.6316	0	0	RS3seq-Chen2013+RS3target	-0.03095	242			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980679	TGGAAATATACATCATAAGA	ATTTTGGAAATATACATCATAAGAGGGCCT	GGG	9	1181	2577	45.8		2.4841	0	0	RS3seq-Chen2013+RS3target	-0.03411	243			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979852	TGACCTGCCCAAGCTGGTGG	CGGCTGACCTGCCCAAGCTGGTGGAGGGGC	AGG	10	1561	2577	60.6		1.6345	0	0	RS3seq-Chen2013+RS3target	-0.03556	244			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982005	AAGTCAGCCACCAGCCCCGA	CAGCAAGTCAGCCACCAGCCCCGAAGGGAA	AGG	6	839	2577	32.6		1.5896	0	0	RS3seq-Chen2013+RS3target	-0.03840	245			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977220	CTGCCCGTCAACGAGTCCTT	CTATCTGCCCGTCAACGAGTCCTTTGGTGA	TGG	14	2378	2577	92.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.03887	246			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980545	CTATACCCCTGGGAAGAAGG	CCAACTATACCCCTGGGAAGAAGGAGGACC	AGG	9	1315	2577	51.0		2.7275	0	0	RS3seq-Chen2013+RS3target	-0.03945	247			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979901	TCCACGGCCACTCTGACAAC	GGCCTCCACGGCCACTCTGACAACAGGGCT	AGG	10	1512	2577	58.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.04001	248			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982878	CCAGACTTTCCAGCGCATCG	TCTACCAGACTTTCCAGCGCATCGTGGAGA	TGG	4	541	2577	21.0		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.04122	249			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980548	CAACTATACCCCTGGGAAGA	GGCCCAACTATACCCCTGGGAAGAAGGAGG	AGG	9	1312	2577	50.9		1.0000	1	0	RS3seq-Chen2013+RS3target	-0.04164	250			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980029	CCCACAAGGCACATCCTCGA	TGTTCCCACAAGGCACATCCTCGATGGGCT	TGG	10	1384	2577	53.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.04265	251			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982360	ACATCTCGGCAAACTGCTTC	ACATACATCTCGGCAAACTGCTTCAGGGTG	AGG	5	677	2577	26.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.04748	252			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977969	ACCTGGCCGAGAAGTACGAG	CGCTACCTGGCCGAGAAGTACGAGTGGGAC	TGG	12	1917	2577	74.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.04752	253			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977824	CTTCCAGTGGGCCACCAAGG	CCGGCTTCCAGTGGGCCACCAAGGAGGTGA	AGG	12	2062	2577	80.0		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.04975	254			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3976720	GACCTGAGGTCCAACACGGG	CGCTGACCTGAGGTCCAACACGGGCGGCCA	CGG	15	2411	2577	93.6		0.6654	0	0	RS3seq-Chen2013+RS3target	-0.05007	255			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982002	GTGGCAGCTTCTTCCCTTCG	GTGCGTGGCAGCTTCTTCCCTTCGGGGCTG	GGG	6	842	2577	32.7		1.8462	0	0	RS3seq-Chen2013+RS3target	-0.05071	256			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980910	CTTCTGGGCCGTCACAGGGG	GGTACTTCTGGGCCGTCACAGGGGAGGGCA	AGG	8	1081	2577	41.9		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.05135	257			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982038	AGGTACTTTGACCCAGCCAA	TGCCAGGTACTTTGACCCAGCCAACGGCAA	CGG	6	806	2577	31.3		0.8972	0	0	RS3seq-Chen2013+RS3target	-0.05284	258			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980065	GCCCATCATCAAGATTGTTC	AGCGGCCCATCATCAAGATTGTTCTGGAAG	TGG	10	1348	2577	52.3		0.8351	0	0	RS3seq-Chen2013+RS3target	-0.05307	259			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977309	GCATCTACGGGGTTTTGAAC	GGTGGCATCTACGGGGTTTTGAACAGGAAG	AGG	14	2289	2577	88.8		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.05487	260			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; poly(T):TTTT
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980590	GATCCTGACCTTCAGGCCAG	CCATGATCCTGACCTTCAGGCCAGTGGAGA	TGG	9	1270	2577	49.3		2.7517	0	0	RS3seq-Chen2013+RS3target	-0.05750	261			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980847	CCCGGACGACGAGGCTGCCA	GGCCCCCGGACGACGAGGCTGCCATGGGTA	TGG	8	1144	2577	44.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.05781	262			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977489	CGGTCAGCACACTGGCATAG	TGGGCGGTCAGCACACTGGCATAGAGGCAG	AGG	13	2189	2577	84.9		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.05915	263			Ancestry variant frequency exceeded threshold; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977341	CTTCTGGCAGTGTCCAGAGC	TTCCCTTCTGGCAGTGTCCAGAGCAGGTGG	AGG	14	2257	2577	87.6		1.8783	1	0	RS3seq-Chen2013+RS3target	-0.06404	264			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979995	GGAACTGGTCCACGCCCACG	ACCAGGAACTGGTCCACGCCCACGAGGCCC	AGG	10	1418	2577	55.0		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.06527	265			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980575	TGGCCTGAAGGTCAGGATCA	CCACTGGCCTGAAGGTCAGGATCATGGGGC	TGG	9	1285	2577	49.9		5.1266	1	1	RS3seq-Chen2013+RS3target	-0.06748	266			Aggregate CFD score > 4.80; Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982414	TTCCAGATCGATCCTGTCCT	ACACTTCCAGATCGATCCTGTCCTCGGTAC	CGG	5	623	2577	24.2		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.06761	267			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980595	TTCTCGGGGCTGGTCTCCAC	AGTCTTCTCGGGGCTGGTCTCCACTGGCCT	TGG	9	1265	2577	49.1		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.07196	268			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976636	TCTCCGCCACCACCTGGCTG	CGGGTCTCCGCCACCACCTGGCTGGGGCGG	GGG	15	2495	2577	96.8		3.2686	2	0	RS3seq-Chen2013+RS3target	-0.07271	269			BsmBI:5'cgTCTC; Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3976724	CGCTGACCTGAGGTCCAACA	TCACCGCTGACCTGAGGTCCAACACGGGCG	CGG	15	2407	2577	93.4		0.1905	0	0	RS3seq-Chen2013+RS3target	-0.07293	270			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982820	GTACCATGATGTTGCCCATG	GGCCGTACCATGATGTTGCCCATGGGGCCG	GGG	4	599	2577	23.2		1.5571	0	0	RS3seq-Chen2013+RS3target	-0.07467	271			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980609	CCTTTGGACGAGTCTTCTCG	TACGCCTTTGGACGAGTCTTCTCGGGGCTG	GGG	9	1251	2577	48.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.07794	272			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3983211	GGGAGTCAATGAGGTTGATG	CCGGGGGAGTCAATGAGGTTGATGAGGAAG	AGG	3	299	2577	11.6		0.3833	0	0	RS3seq-Chen2013+RS3target	-0.08054	273			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980043	CCTCGATGGGCTCCACGTAG	ACATCCTCGATGGGCTCCACGTAGCGGCCC	CGG	10	1370	2577	53.2		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.08404	274			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3976597	AAGCGCAAGGGCCTGAAAGA	CCGCAAGCGCAAGGGCCTGAAAGAAGGCAT	AGG	15	2534	2577	98.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.09009	275			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980691	ATTAAAAGCTGTGACCCCAA	AGGCATTAAAAGCTGTGACCCCAAAGGCCC	AGG	9	1169	2577	45.4		2.3128	0	0	RS3seq-Chen2013+RS3target	-0.09431	276			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980008	TGTGCCTTGTGGGAACATTG	AGGATGTGCCTTGTGGGAACATTGTGGGCC	TGG	10	1405	2577	54.5		0.5723	0	0	RS3seq-Chen2013+RS3target	-0.09473	277			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977529	CGACGCCATCCACCGCGGAG	ACGCCGACGCCATCCACCGCGGAGGGGGCC	GGG	13	2149	2577	83.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.09838	278			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982880	TCACGTTCTCCACGATGCGC	ACGTTCACGTTCTCCACGATGCGCTGGAAA	TGG	4	539	2577	20.9		0.0390	0	0	RS3seq-Chen2013+RS3target	-0.1018	279			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3981953	CCCTCACTCACCTTGAAGAT	GCAGCCCTCACTCACCTTGAAGATGGGGTC	GGG	6	891	2577	34.6		0.0390	0	0	RS3seq-Chen2013+RS3target	-0.1060	280			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980957	ATGCGCCGCTGGCTGCCTGC	TGTGATGCGCCGCTGGCTGCCTGCCGGAGA	CGG	8	1034	2577	40.1		1.6396	0	0	RS3seq-Chen2013+RS3target	-0.1086	281			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977445	CTGGATCTCCACAAGGTAGA	TCACCTGGATCTCCACAAGGTAGATGGGCT	TGG	13	2233	2577	86.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1090	282			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3976609	GCGGAGACCCGCAAGCGCAA	GGTGGCGGAGACCCGCAAGCGCAAGGGCCT	GGG	15	2522	2577	97.9		0.3077	0	0	RS3seq-Chen2013+RS3target	-0.1109	283			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980688	ACATCATAAGAGGGCCTTTG	ATATACATCATAAGAGGGCCTTTGGGGTCA	GGG	9	1172	2577	45.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1160	284			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984203	GAAGCGTGTCTCCCCGGCCC	CAGTGAAGCGTGTCTCCCCGGCCCGGGCCG	GGG	2	151	2577	5.9		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1204	285			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979855	GGCTGACCTGCCCAAGCTGG	ACCCGGCTGACCTGCCCAAGCTGGTGGAGG	TGG	10	1558	2577	60.5		1.7123	0	0	RS3seq-Chen2013+RS3target	-0.1236	286			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984292	GGGCGATGACAGACATGTTG	ACGTGGGCGATGACAGACATGTTGCGGATG	CGG	2	62	2577	2.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1260	287			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982260	AGGACATGATGAAGAAGCTG	GTAGAGGACATGATGAAGAAGCTGTGGGGT	TGG	5	777	2577	30.2		2.9780	1	0	RS3seq-Chen2013+RS3target	-0.1308	288			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979358	CTGCCTGAAGGACCTGGAGG	AGATCTGCCTGAAGGACCTGGAGGAGGACC	AGG	11	1684	2577	65.3		4.3475	0	0	RS3seq-Chen2013+RS3target	-0.1352	289			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980859	CATGGCAGCCTCGTCGTCCG	TACCCATGGCAGCCTCGTCGTCCGGGGGCC	GGG	8	1132	2577	43.9		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1383	290			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977549	GGTGGATGGCGTCGGCGTGC	CCGCGGTGGATGGCGTCGGCGTGCAGGGTG	AGG	13	2129	2577	82.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1389	291			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982925	GCTCCAGCTGCAGCTCCAGC	TCGGGCTCCAGCTGCAGCTCCAGCAGGGCG	AGG	4	494	2577	19.2		10.7989	2	1	RS3seq-Chen2013+RS3target	-0.1402	292			Aggregate CFD score > 4.80; Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982323	TGGCCAAGTTCGCCGCCAAG	TATGTGGCCAAGTTCGCCGCCAAGGGGGAG	GGG	5	714	2577	27.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1458	293			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3984233	GACAGACTCCCTGGTGTGCA	CGCTGACAGACTCCCTGGTGTGCAAGGCGG	AGG	2	121	2577	4.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1486	294			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3983148	CCAATGCGCCATCGGTGACT	ACCACCAATGCGCCATCGGTGACTCGGAGG	CGG	3	362	2577	14.0		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1486	295			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979331	TCACTGGCGCCTCACCTTGA	CTGGTCACTGGCGCCTCACCTTGATGGGGA	TGG	11	1711	2577	66.4		0.5385	0	0	RS3seq-Chen2013+RS3target	-0.1506	296			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980625	AAAGGTCGGTTCTACGCCTT	CGACAAAGGTCGGTTCTACGCCTTTGGACG	TGG	9	1235	2577	47.9		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1515	297			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982957	ATCTTGTTCATCATCAGCAC	GTCCATCTTGTTCATCATCAGCACAGGCTT	AGG	4	462	2577	17.9		0.5833	0	0	RS3seq-Chen2013+RS3target	-0.1547	298			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3981339	GCAAACCCCTGCTGAAGGTG	GAAGGCAAACCCCTGCTGAAGGTGCGGCCT	CGG	7	1011	2577	39.2		1.1209	0	0	RS3seq-Chen2013+RS3target	-0.1576	299			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978097	CAGCCGGTTGTGCTTGTTGG	TGTACAGCCGGTTGTGCTTGTTGGGGGACT	GGG	12	1789	2577	69.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1627	300			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3978033	CTGGCCGAGGACATCGATAA	CGGCCTGGCCGAGGACATCGATAAAGGCGA	AGG	12	1853	2577	71.9		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1670	301			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976630	TGCGGGTCTCCGCCACCACC	CGCTTGCGGGTCTCCGCCACCACCTGGCTG	TGG	15	2501	2577	97.1		1.1364	1	0	RS3seq-Chen2013+RS3target	-0.1673	302			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980953	TGCAACAAGGCGTCTCCGGC	CATCTGCAACAAGGCGTCTCCGGCAGGCAG	AGG	8	1038	2577	40.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1684	303			BsmBI:CGTCTC; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977530	CCGACGCCATCCACCGCGGA	CACGCCGACGCCATCCACCGCGGAGGGGGC	GGG	13	2148	2577	83.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1692	304			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979897	CGTCAGCCCTGTTGTCAGAG	TCAGCGTCAGCCCTGTTGTCAGAGTGGCCG	TGG	10	1516	2577	58.8		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1733	305			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977962	CGAGAAGTACGAGTGGGACG	TGGCCGAGAAGTACGAGTGGGACGTGGCTG	TGG	12	1924	2577	74.7		0.9412	0	0	RS3seq-Chen2013+RS3target	-0.1761	306			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980874	CCGCTGCGAGCTCCTGTACG	AGTACCGCTGCGAGCTCCTGTACGAGGGGC	AGG	8	1117	2577	43.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1805	307			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977541	CCCTCCGCGGTGGATGGCGT	GGCCCCCTCCGCGGTGGATGGCGTCGGCGT	CGG	13	2137	2577	82.9		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1836	308			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978052	GATGTCCTCGGCCAGGCCGT	TATCGATGTCCTCGGCCAGGCCGTCGGGGA	CGG	12	1834	2577	71.2		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1916	309			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976664	GCTGTTGTCGAAGGGGTCTC	GGCTGCTGTTGTCGAAGGGGTCTCCGGGCA	CGG	15	2467	2577	95.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1923	310			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980030	CCACAAGGCACATCCTCGAT	GTTCCCACAAGGCACATCCTCGATGGGCTC	GGG	10	1383	2577	53.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1929	311			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3983007	CCCAGGCGTGTGCGTGCAGA	TGTCCCCAGGCGTGTGCGTGCAGACGGAGA	CGG	4	412	2577	16.0		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1941	312			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979998	GGGAACATTGTGGGCCTCGT	TTGTGGGAACATTGTGGGCCTCGTGGGCGT	GGG	10	1415	2577	54.9		0.7762	0	0	RS3seq-Chen2013+RS3target	-0.2075	313			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980918	CCGTCACAGGGGAGGGCAGG	TGGGCCGTCACAGGGGAGGGCAGGTGGATG	TGG	8	1073	2577	41.6		6.0146	0	0	RS3seq-Chen2013+RS3target	-0.2084	314			Aggregate CFD score > 4.80; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980968	CGCAGGCTGTGATGCGCCGC	CCCCCGCAGGCTGTGATGCGCCGCTGGCTG	TGG	8	1023	2577	39.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2084	315			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982413	AAGCCCACGGTACCGAGGAC	CCCAAAGCCCACGGTACCGAGGACAGGATC	AGG	5	624	2577	24.2		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2111	316			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3976686	CCCAGTGTGTGTTTGACCAC	TTCCCCCAGTGTGTGTTTGACCACTGGCAG	TGG	15	2445	2577	94.9		0.8571	0	0	RS3seq-Chen2013+RS3target	-0.2115	317			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982381	GGGTGAAGGCCCACCCGTGG	TTCAGGGTGAAGGCCCACCCGTGGAGGCCA	AGG	5	656	2577	25.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2126	318			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980610	GCCTTTGGACGAGTCTTCTC	CTACGCCTTTGGACGAGTCTTCTCGGGGCT	GGG	9	1250	2577	48.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2244	319			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980019	CCCATCGAGGATGTGCCTTG	GGAGCCCATCGAGGATGTGCCTTGTGGGAA	TGG	10	1394	2577	54.1		0.5000	0	0	RS3seq-Chen2013+RS3target	-0.2255	320			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980583	GCCCCATGATCCTGACCTTC	TTGGGCCCCATGATCCTGACCTTCAGGCCA	AGG	9	1277	2577	49.6		0.8831	0	0	RS3seq-Chen2013+RS3target	-0.2289	321			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977925	ATCTGGTGCTTTGGGCCCGA	CAAGATCTGGTGCTTTGGGCCCGACGGCAC	CGG	12	1961	2577	76.1		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2336	322			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976669	TGTCGAAGGGGTCTCCGGGC	CTGTTGTCGAAGGGGTCTCCGGGCAGGATC	AGG	15	2462	2577	95.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2347	323			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3983115	GTGGTGGTGGACTGCGTGTC	ATTGGTGGTGGTGGACTGCGTGTCAGGTAA	AGG	3	395	2577	15.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2354	324			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979837	GGTGGAGGGGCTGAAGCGGC	AGCTGGTGGAGGGGCTGAAGCGGCTGGCCA	TGG	10	1576	2577	61.2		3.2178	0	0	RS3seq-Chen2013+RS3target	-0.2420	325			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977227	CTCACCAAAGGACTCGTTGA	GGCACTCACCAAAGGACTCGTTGACGGGCA	CGG	14	2371	2577	92.0		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2452	326			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977466	GGGCTCCATGAGGCGTGGCT	AGATGGGCTCCATGAGGCGTGGCTGGGCGG	GGG	13	2212	2577	85.8		0.9412	0	0	RS3seq-Chen2013+RS3target	-0.2462	327			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978158	GTCTCGCGGTACGAGACGAC	GACCGTCTCGCGGTACGAGACGACCGGGTC	CGG	12	1728	2577	67.1		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2551	328			BsmBI:GAGACG; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979957	CGCGTGCTCGAAGGTGGTGA	TGTGCGCGTGCTCGAAGGTGGTGATGGTGC	TGG	10	1456	2577	56.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2568	329			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984143	GGGAGCTCACGTTGACTTGA	CACAGGGAGCTCACGTTGACTTGATGGTGA	TGG	2	211	2577	8.2		0.2609	0	0	RS3seq-Chen2013+RS3target	-0.2571	330			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982845	GCTCTCGCCCTCGCCGTAGG	GGCCGCTCTCGCCCTCGCCGTAGGTGGAGA	TGG	4	574	2577	22.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2584	331			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979855	CTTCAGCCCCTCCACCAGCT	GCCGCTTCAGCCCCTCCACCAGCTTGGGCA	TGG	10	1558	2577	60.5		2.1609	0	0	RS3seq-Chen2013+RS3target	-0.2657	332			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980894	GGAGCTCGCAGCGGTACTTC	TACAGGAGCTCGCAGCGGTACTTCTGGGCC	TGG	8	1097	2577	42.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2697	333			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3976631	CAGCCGCCCCAGCCAGGTGG	ACAGCAGCCGCCCCAGCCAGGTGGTGGCGG	TGG	15	2500	2577	97.0		0.4000	0	0	RS3seq-Chen2013+RS3target	-0.2713	334			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982408	ACCCAAAGCCCACGGTACCG	CCAGACCCAAAGCCCACGGTACCGAGGACA	AGG	5	629	2577	24.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2750	335			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982308	CCAAGGGGGAGGGCCAGTTG	GCCGCCAAGGGGGAGGGCCAGTTGGGGCCT	GGG	5	729	2577	28.3		1.1936	0	0	RS3seq-Chen2013+RS3target	-0.2770	336			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3983232	TTCATCAAGCAGAGCAAGGA	GAACTTCATCAAGCAGAGCAAGGACGGTGC	CGG	3	278	2577	10.8		1.2640	0	0	RS3seq-Chen2013+RS3target	-0.2785	337			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980582	TCTCCACTGGCCTGAAGGTC	CTGGTCTCCACTGGCCTGAAGGTCAGGATC	AGG	9	1278	2577	49.6		1.9301	1	0	RS3seq-Chen2013+RS3target	-0.2842	338			BsmBI:5'cgTCTC; Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977271	TTCGAGGAGTCCCAGGTGGC	CGTGTTCGAGGAGTCCCAGGTGGCCGGCAC	CGG	14	2327	2577	90.3		3.6102	0	0	RS3seq-Chen2013+RS3target	-0.2917	339			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978094	GTACAGCCGGTTGTGCTTGT	TCATGTACAGCCGGTTGTGCTTGTTGGGGG	TGG	12	1792	2577	69.5		0.5333	0	0	RS3seq-Chen2013+RS3target	-0.2956	340			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979868	ACCAGCTTGGGCAGGTCAGC	CTCCACCAGCTTGGGCAGGTCAGCCGGGTT	CGG	10	1545	2577	60.0		0.7857	0	0	RS3seq-Chen2013+RS3target	-0.2991	341			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978040	CTCGCCTTTATCGATGTCCT	ACACCTCGCCTTTATCGATGTCCTCGGCCA	CGG	12	1846	2577	71.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3042	342			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982033	TGACTTGCTGAACTTGCCGT	TGGCTGACTTGCTGAACTTGCCGTTGGCTG	TGG	6	811	2577	31.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3068	343			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982367	GGCAAACTGCTTCAGGGTGA	TCTCGGCAAACTGCTTCAGGGTGAAGGCCC	AGG	5	670	2577	26.0		2.2139	0	0	RS3seq-Chen2013+RS3target	-0.3084	344			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977278	CCACGTGTTCGAGGAGTCCC	GGGGCCACGTGTTCGAGGAGTCCCAGGTGG	AGG	14	2320	2577	90.0		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3112	345			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982296	GCCAGTTGGGGCCTGCCGAG	GAGGGCCAGTTGGGGCCTGCCGAGCGGGCC	CGG	5	741	2577	28.8		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3137	346			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977893	CTGCACACCCTTGGTGATGT	GGTACTGCACACCCTTGGTGATGTCGGTGA	CGG	12	1993	2577	77.3		0.6828	0	0	RS3seq-Chen2013+RS3target	-0.3140	347			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982842	CATCATCTCCACCTACGGCG	ACGTCATCATCTCCACCTACGGCGAGGGCG	AGG	4	577	2577	22.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3219	348			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977610	CTCCTCACACAGTGCGCCCT	TGTTCTCCTCACACAGTGCGCCCTGGGGGA	GGG	13	2068	2577	80.2		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3306	349			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977469	CTCCATGAGGCGTGGCTGGG	TGGGCTCCATGAGGCGTGGCTGGGCGGTCA	CGG	13	2209	2577	85.7		2.5588	0	0	RS3seq-Chen2013+RS3target	-0.3317	350			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978059	TCGGCCAGGCCGTCGGGGAA	GTCCTCGGCCAGGCCGTCGGGGAAGGGCCG	GGG	12	1827	2577	70.9		0.9846	0	0	RS3seq-Chen2013+RS3target	-0.3389	351			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977974	CACGTCCCACTCGTACTTCT	CAGCCACGTCCCACTCGTACTTCTCGGCCA	CGG	12	1912	2577	74.2		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3396	352			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979972	GGACCAGTTCCTGGTGAAGA	GCGTGGACCAGTTCCTGGTGAAGACGGGCA	CGG	10	1441	2577	55.9		1.1050	0	0	RS3seq-Chen2013+RS3target	-0.3408	353			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979858	CCCGGCTGACCTGCCCAAGC	AGAACCCGGCTGACCTGCCCAAGCTGGTGG	TGG	10	1555	2577	60.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3417	354			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982393	GGTACCGTGGGCTTTGGGTC	CCTCGGTACCGTGGGCTTTGGGTCTGGCCT	TGG	5	644	2577	25.0		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3449	355			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977460	GACCGCCCAGCCACGCCTCA	TGCTGACCGCCCAGCCACGCCTCATGGAGC	TGG	13	2218	2577	86.1		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3472	356			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980856	CGAGGGGCCCCCGGACGACG	TGTACGAGGGGCCCCCGGACGACGAGGCTG	AGG	8	1135	2577	44.0		0.1538	0	0	RS3seq-Chen2013+RS3target	-0.3478	357			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979999	TGGGAACATTGTGGGCCTCG	CTTGTGGGAACATTGTGGGCCTCGTGGGCG	TGG	10	1414	2577	54.9		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3501	358			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3976559	CAACTTCCTGGACAAATTGT	TGGACAACTTCCTGGACAAATTGTAGGCGG	AGG	15	2572	2577	99.8		1.1364	1	0	RS3seq-Chen2013+RS3target	-0.3542	359			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977911	GTCGGTGAGGATGTTGGGGC	TGATGTCGGTGAGGATGTTGGGGCCGGTGC	CGG	12	1975	2577	76.6		1.5188	0	0	RS3seq-Chen2013+RS3target	-0.3615	360			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982823	GAGGGCGAGAGCGGCCCCAT	CGGCGAGGGCGAGAGCGGCCCCATGGGCAA	GGG	4	596	2577	23.1		0.2069	0	0	RS3seq-Chen2013+RS3target	-0.3756	361			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980858	CCATGGCAGCCTCGTCGTCC	GTACCCATGGCAGCCTCGTCGTCCGGGGGC	GGG	8	1133	2577	44.0		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3800	362			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977289	CCTGGGACTCCTCGAACACG	GCCACCTGGGACTCCTCGAACACGTGGCCC	TGG	14	2309	2577	89.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3852	363			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980905	CGGTACTTCTGGGCCGTCAC	GCAGCGGTACTTCTGGGCCGTCACAGGGGA	AGG	8	1086	2577	42.1		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3854	364			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979366	GGTCCTCCTCCAGGTCCTTC	GCGTGGTCCTCCTCCAGGTCCTTCAGGCAG	AGG	11	1676	2577	65.0		2.9994	0	0	RS3seq-Chen2013+RS3target	-0.3907	365			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3981371	CAAACTGGACAGCGAGGACA	ACATCAAACTGGACAGCGAGGACAAGGACA	AGG	7	979	2577	38.0		3.5302	0	0	RS3seq-Chen2013+RS3target	-0.3925	366			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 875, 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982908	GGAGCTGCAGCTGGAGCCCG	TGCTGGAGCTGCAGCTGGAGCCCGAGGAGC	AGG	4	511	2577	19.8		5.6273	1	1	RS3seq-Chen2013+RS3target	-0.3955	367			Aggregate CFD score > 4.80; Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977998	CCGTCAGGAGCTCAAGCAGC	CCGCCCGTCAGGAGCTCAAGCAGCGGGCGC	GGG	12	1888	2577	73.3		1.9650	1	0	RS3seq-Chen2013+RS3target	-0.3971	368			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982384	GGCTTTGGGTCTGGCCTCCA	CGTGGGCTTTGGGTCTGGCCTCCACGGGTG	CGG	5	653	2577	25.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3976	369			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984299	GACAGACATGTTGCGGATGT	CGATGACAGACATGTTGCGGATGTTGGCCT	TGG	2	55	2577	2.1		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4010	370			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982807	CCATGGGCAACATCATGGTA	GGCCCCATGGGCAACATCATGGTACGGCCC	CGG	4	612	2577	23.7		0.2759	0	0	RS3seq-Chen2013+RS3target	-0.4069	371			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984251	CACCAGGGAGTCTGTCAGCG	TGCACACCAGGGAGTCTGTCAGCGTGGACT	TGG	2	103	2577	4.0		0.1250	0	0	RS3seq-Chen2013+RS3target	-0.4121	372			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3984209	GGGCATCATCGCCTCGGCCC	AGGCGGGCATCATCGCCTCGGCCCGGGCCG	GGG	2	145	2577	5.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4127	373			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980911	TTCTGGGCCGTCACAGGGGA	GTACTTCTGGGCCGTCACAGGGGAGGGCAG	GGG	8	1080	2577	41.9		0.8571	0	0	RS3seq-Chen2013+RS3target	-0.4188	374			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979860	GCCCCTCCACCAGCTTGGGC	TTCAGCCCCTCCACCAGCTTGGGCAGGTCA	AGG	10	1553	2577	60.3		1.2017	0	0	RS3seq-Chen2013+RS3target	-0.4206	375			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977919	TGCTTTGGGCCCGACGGCAC	CTGGTGCTTTGGGCCCGACGGCACCGGCCC	CGG	12	1967	2577	76.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4222	376			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980532	TCTGGATTGGCTTCAGGTAG	CACCTCTGGATTGGCTTCAGGTAGAGGTCC	AGG	9	1328	2577	51.5		0.0390	0	0	RS3seq-Chen2013+RS3target	-0.4234	377			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979825	CTGCACCATGGGGTCGGACT	CCACCTGCACCATGGGGTCGGACTTGGCCA	TGG	10	1588	2577	61.6		0.2222	0	0	RS3seq-Chen2013+RS3target	-0.4242	378			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977272	CATGGGGGTGCCGGCCACCT	CAAACATGGGGGTGCCGGCCACCTGGGACT	GGG	14	2326	2577	90.3		0.2500	0	0	RS3seq-Chen2013+RS3target	-0.4286	379			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3976610	GGCGGAGACCCGCAAGCGCA	TGGTGGCGGAGACCCGCAAGCGCAAGGGCC	AGG	15	2521	2577	97.8		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4288	380			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982378	TCAGGGTGAAGGCCCACCCG	TGCTTCAGGGTGAAGGCCCACCCGTGGAGG	TGG	5	659	2577	25.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4338	381			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977322	CAGGTGGTCGGTGGCATCTA	AGAGCAGGTGGTCGGTGGCATCTACGGGGT	CGG	14	2276	2577	88.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4357	382			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3983018	CCGTCTGCACGCACACGCCT	GTCTCCGTCTGCACGCACACGCCTGGGGAC	GGG	4	401	2577	15.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4359	383			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984172	AACGCTCCTGCTCGTCCTTC	ATGCAACGCTCCTGCTCGTCCTTCCGGGTA	CGG	2	182	2577	7.1		1.4890	0	0	RS3seq-Chen2013+RS3target	-0.4442	384			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980574	GGCCTGAAGGTCAGGATCAT	CACTGGCCTGAAGGTCAGGATCATGGGGCC	GGG	9	1286	2577	49.9		1.2762	0	0	RS3seq-Chen2013+RS3target	-0.4471	385			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3983188	CGAGGAGAAGTCGACATGCC	CCTCCGAGGAGAAGTCGACATGCCCGGGGG	CGG	3	322	2577	12.5		0.4444	0	0	RS3seq-Chen2013+RS3target	-0.4491	386			Ancestry variant frequency exceeded threshold; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982295	CCAGTTGGGGCCTGCCGAGC	AGGGCCAGTTGGGGCCTGCCGAGCGGGCCA	GGG	5	742	2577	28.8		0.7143	0	0	RS3seq-Chen2013+RS3target	-0.4500	387			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979876	GGGCAGGTCAGCCGGGTTCT	GCTTGGGCAGGTCAGCCGGGTTCTTGGCCT	TGG	10	1537	2577	59.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4557	388			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3983128	CGATGGCGCATTGGTGGTGG	TCACCGATGGCGCATTGGTGGTGGTGGACT	TGG	3	382	2577	14.8		1.1373	0	0	RS3seq-Chen2013+RS3target	-0.4581	389			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982322	GGCCAAGTTCGCCGCCAAGG	ATGTGGCCAAGTTCGCCGCCAAGGGGGAGG	GGG	5	715	2577	27.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4697	390			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980662	TATGATGTATATTTCCAAAA	CTCTTATGATGTATATTTCCAAAATGGTGC	TGG	9	1198	2577	46.5		1.3114	0	0	RS3seq-Chen2013+RS3target	-0.4787	391			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979810	GTCCGACCCCATGGTGCAGG	CCAAGTCCGACCCCATGGTGCAGGTGGGCA	TGG	10	1603	2577	62.2		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4856	392			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982309	GCCAAGGGGGAGGGCCAGTT	CGCCGCCAAGGGGGAGGGCCAGTTGGGGCC	GGG	5	728	2577	28.2		0.4092	0	0	RS3seq-Chen2013+RS3target	-0.4911	393			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982251	TGAAGAAGCTGTGGGGTGAC	ATGATGAAGAAGCTGTGGGGTGACAGGTGA	AGG	5	786	2577	30.5		1.2256	0	0	RS3seq-Chen2013+RS3target	-0.4914	394			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977986	CAAGCAGCGGGCGCGCTACC	AGCTCAAGCAGCGGGCGCGCTACCTGGCCG	TGG	12	1900	2577	73.7		0.6762	0	0	RS3seq-Chen2013+RS3target	-0.4928	395			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977528	GACGCCATCCACCGCGGAGG	CGCCGACGCCATCCACCGCGGAGGGGGCCA	GGG	13	2150	2577	83.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4930	396			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977921	ATGTTGGGGCCGGTGCCGTC	GAGGATGTTGGGGCCGGTGCCGTCGGGCCC	GGG	12	1965	2577	76.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4966	397			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984262	CTGTCAGCGTGGACTTGCCA	GAGTCTGTCAGCGTGGACTTGCCATGGTCC	TGG	2	92	2577	3.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.5012	398			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976564	GGGCCGCCTACAATTTGTCC	GGAAGGGCCGCCTACAATTTGTCCAGGAAG	AGG	15	2567	2577	99.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.5031	399			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977561	CGGCGTGCAGGGTGACGTCG	GCGTCGGCGTGCAGGGTGACGTCGTGGACG	TGG	13	2117	2577	82.1		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.5033	400			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3978071	ACCGGCTGTACATGAAGGCG	CACAACCGGCTGTACATGAAGGCGCGGCCC	CGG	12	1815	2577	70.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.5058	401			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982319	CCCCAACTGGCCCTCCCCCT	CAGGCCCCAACTGGCCCTCCCCCTTGGCGG	TGG	5	718	2577	27.9		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.5080	402			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982406	CGATCCTGTCCTCGGTACCG	AGATCGATCCTGTCCTCGGTACCGTGGGCT	TGG	5	631	2577	24.5		0.3846	0	0	RS3seq-Chen2013+RS3target	-0.5089	403			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979340	CCTCACCTTGATGGGGATGC	GGCGCCTCACCTTGATGGGGATGCAGGCGT	AGG	11	1702	2577	66.0		0.6667	0	0	RS3seq-Chen2013+RS3target	-0.5141	404			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3981967	CACCTTCTGCCAGCTGATCC	CACGCACCTTCTGCCAGCTGATCCTGGACC	TGG	6	877	2577	34.0		2.2711	0	0	RS3seq-Chen2013+RS3target	-0.5159	405			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976729	GGCCGCCCGTGTTGGACCTC	GCCTGGCCGCCCGTGTTGGACCTCAGGTCA	AGG	15	2402	2577	93.2		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.5321	406			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982819	CGTACCATGATGTTGCCCAT	GGGCCGTACCATGATGTTGCCCATGGGGCC	GGG	4	600	2577	23.3		0.6257	0	0	RS3seq-Chen2013+RS3target	-0.5328	407			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979430	TCCCTTTCAGTGCATCATCG	CGCATCCCTTTCAGTGCATCATCGAGGAGT	AGG	11	1612	2577	62.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.5366	408			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976582	CCAGGAAGTTGTCCAGGGCA	TTGTCCAGGAAGTTGTCCAGGGCAGGGATG	GGG	15	2549	2577	98.9		1.1190	0	0	RS3seq-Chen2013+RS3target	-0.5372	409			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982818	CCGTACCATGATGTTGCCCA	AGGGCCGTACCATGATGTTGCCCATGGGGC	TGG	4	601	2577	23.3		0.5000	0	0	RS3seq-Chen2013+RS3target	-0.5375	410			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976577	TTTGTCCAGGAAGTTGTCCA	ACAATTTGTCCAGGAAGTTGTCCAGGGCAG	GGG	15	2554	2577	99.1		0.4136	0	0	RS3seq-Chen2013+RS3target	-0.5380	411			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977943	GCCCAAAGCACCAGATCTTG	TCGGGCCCAAAGCACCAGATCTTGCGGGCC	CGG	12	1943	2577	75.4		2.4383	0	0	RS3seq-Chen2013+RS3target	-0.5434	412			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977301	GGGGTTTTGAACAGGAAGCG	CTACGGGGTTTTGAACAGGAAGCGGGGCCA	GGG	14	2297	2577	89.1		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.5523	413			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; poly(T):TTTT
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977271	ACATGGGGGTGCCGGCCACC	ACAAACATGGGGGTGCCGGCCACCTGGGAC	TGG	14	2327	2577	90.3		0.2667	0	0	RS3seq-Chen2013+RS3target	-0.5564	414			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978095	TACAGCCGGTTGTGCTTGTT	CATGTACAGCCGGTTGTGCTTGTTGGGGGA	GGG	12	1791	2577	69.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.5799	415			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977827	CGGCTTCCAGTGGGCCACCA	TGGCCGGCTTCCAGTGGGCCACCAAGGAGG	AGG	12	2059	2577	79.9		0.5000	0	0	RS3seq-Chen2013+RS3target	-0.5832	416			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978081	GCCGCGCCTTCATGTACAGC	AAGGGCCGCGCCTTCATGTACAGCCGGTTG	CGG	12	1805	2577	70.0		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.5879	417			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977868	CACTGTCCTTGATCTCGTTG	ACCACACTGTCCTTGATCTCGTTGAGGTAC	AGG	12	2018	2577	78.3		0.2500	0	0	RS3seq-Chen2013+RS3target	-0.5909	418			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982917	CGCCCTGCTGGAGCTGCAGC	ACCGCGCCCTGCTGGAGCTGCAGCTGGAGC	TGG	4	502	2577	19.5		7.5523	1	1	RS3seq-Chen2013+RS3target	-0.5921	419			Aggregate CFD score > 4.80; Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980526	CTCACCTCTGGATTGGCTTC	GCTGCTCACCTCTGGATTGGCTTCAGGTAG	AGG	9	1334	2577	51.8		1.3952	0	0	RS3seq-Chen2013+RS3target	-0.5935	420			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977956	GTACGAGTGGGACGTGGCTG	AGAAGTACGAGTGGGACGTGGCTGAGGCCC	AGG	12	1930	2577	74.9		0.1333	0	0	RS3seq-Chen2013+RS3target	-0.5974	421			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980560	CTTCTTCCCAGGGGTATAGT	CCTCCTTCTTCCCAGGGGTATAGTTGGGCC	TGG	9	1300	2577	50.4		1.8398	0	0	RS3seq-Chen2013+RS3target	-0.6037	422			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977601	CCCCCAGGGCGCACTGTGTG	CCCTCCCCCAGGGCGCACTGTGTGAGGAGA	AGG	13	2077	2577	80.6		0.9071	0	0	RS3seq-Chen2013+RS3target	-0.6044	423			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984271	TGGACTTGCCATGGTCCACG	AGCGTGGACTTGCCATGGTCCACGTGGGCG	TGG	2	83	2577	3.2		0.9474	0	0	RS3seq-Chen2013+RS3target	-0.6060	424			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982398	TCCTCGGTACCGTGGGCTTT	CCTGTCCTCGGTACCGTGGGCTTTGGGTCT	GGG	5	639	2577	24.8		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.6088	425			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982306	CCCGCTCGGCAGGCCCCAAC	TTGGCCCGCTCGGCAGGCCCCAACTGGCCC	TGG	5	731	2577	28.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.6135	426			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3981398	AGCAAAACTGATAGAGAAAC	AGACAGCAAAACTGATAGAGAAACTGGACA	TGG	7	952	2577	36.9		0.7143	0	0	RS3seq-Chen2013+RS3target	-0.6285	427			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 875, 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3978166	CTTTTTGCAGAAATCTGACC	ACCTCTTTTTGCAGAAATCTGACCCGGTCG	CGG	12	1720	2577	66.7		0.2593	0	0	RS3seq-Chen2013+RS3target	-0.6393	428			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; poly(T):TTTTT
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3981425	GATCATGAATTTCAAGAAAG	ATGCGATCATGAATTTCAAGAAAGAGGAGA	AGG	7	925	2577	35.9		2.4607	0	0	RS3seq-Chen2013+RS3target	-0.6469	429			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 875, 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3983131	CACCGATGGCGCATTGGTGG	GAGTCACCGATGGCGCATTGGTGGTGGTGG	TGG	3	379	2577	14.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.6487	430			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979332	CACTGGCGCCTCACCTTGAT	TGGTCACTGGCGCCTCACCTTGATGGGGAT	GGG	11	1710	2577	66.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.6489	431			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976635	GTCTCCGCCACCACCTGGCT	GCGGGTCTCCGCCACCACCTGGCTGGGGCG	GGG	15	2496	2577	96.9		0.8696	0	0	RS3seq-Chen2013+RS3target	-0.6497	432			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3978028	CGAGGACATCGATAAAGGCG	TGGCCGAGGACATCGATAAAGGCGAGGTGT	AGG	12	1858	2577	72.1		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.6562	433			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982318	AAGTTCGCCGCCAAGGGGGA	GGCCAAGTTCGCCGCCAAGGGGGAGGGCCA	GGG	5	719	2577	27.9		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.6641	434			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3976672	GACCACTGGCAGATCCTGCC	GTTTGACCACTGGCAGATCCTGCCCGGAGA	CGG	15	2459	2577	95.4		0.6250	0	0	RS3seq-Chen2013+RS3target	-0.6703	435			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3983017	TCCGTCTGCACGCACACGCC	TGTCTCCGTCTGCACGCACACGCCTGGGGA	TGG	4	402	2577	15.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.6759	436			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3978013	AGGCGAGGTGTCCGCCCGTC	ATAAAGGCGAGGTGTCCGCCCGTCAGGAGC	AGG	12	1873	2577	72.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.6769	437			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979345	CCTTGATGGGGATGCAGGCG	CTCACCTTGATGGGGATGCAGGCGTGGTCC	TGG	11	1697	2577	65.9		0.4286	0	0	RS3seq-Chen2013+RS3target	-0.6868	438			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982380	TTGGGTCTGGCCTCCACGGG	GGCTTTGGGTCTGGCCTCCACGGGTGGGCC	TGG	5	657	2577	25.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.6880	439			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982812	CGGCCCCATGGGCAACATCA	AGAGCGGCCCCATGGGCAACATCATGGTAC	TGG	4	607	2577	23.6		1.8648	0	0	RS3seq-Chen2013+RS3target	-0.6922	440			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3984229	GACTCCCTGGTGTGCAAGGC	GACAGACTCCCTGGTGTGCAAGGCGGGCAT	GGG	2	125	2577	4.9		63.9890	1	0	RS3seq-Chen2013+RS3target	-0.6931	441			Aggregate CFD score > 4.80; Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982001	CGTGGCAGCTTCTTCCCTTC	GGTGCGTGGCAGCTTCTTCCCTTCGGGGCT	GGG	6	843	2577	32.7		0.6923	0	0	RS3seq-Chen2013+RS3target	-0.7080	442			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978009	CCCGCTGCTTGAGCTCCTGA	CGCGCCCGCTGCTTGAGCTCCTGACGGGCG	CGG	12	1877	2577	72.8		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.7177	443			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3976556	CTTCCTGGACAAATTGTAGG	ACAACTTCCTGGACAAATTGTAGGCGGCCC	CGG	15	2575	2577	99.9		0.0222	0	0	RS3seq-Chen2013+RS3target	-0.7284	444			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977254	GGCCGGCACCCCCATGTTTG	AGGTGGCCGGCACCCCCATGTTTGTGGTCA	TGG	14	2344	2577	91.0		0.8667	0	0	RS3seq-Chen2013+RS3target	-0.7538	445			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977934	GCCCGCAAGATCTGGTGCTT	TGAGGCCCGCAAGATCTGGTGCTTTGGGCC	TGG	12	1952	2577	75.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.7623	446			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976657	GGCGGCTGCTGTTGTCGAAG	CTGGGGCGGCTGCTGTTGTCGAAGGGGTCT	GGG	15	2474	2577	96.0		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.7625	447			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977303	ACGGGGTTTTGAACAGGAAG	ATCTACGGGGTTTTGAACAGGAAGCGGGGC	CGG	14	2295	2577	89.1		2.2875	1	0	RS3seq-Chen2013+RS3target	-0.7705	448			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; poly(T):TTTT
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982832	ACCTACGGCGAGGGCGAGAG	CTCCACCTACGGCGAGGGCGAGAGCGGCCC	CGG	4	587	2577	22.8		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.7712	449			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976681	CTCCGGGCAGGATCTGCCAG	GGGTCTCCGGGCAGGATCTGCCAGTGGTCA	TGG	15	2450	2577	95.1		1.0000	1	0	RS3seq-Chen2013+RS3target	-0.7762	450			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3983269	CTCCCTCTTCTACGAGCTCT	CCATCTCCCTCTTCTACGAGCTCTCGGAGA	CGG	3	241	2577	9.4		0.3846	0	0	RS3seq-Chen2013+RS3target	-0.7849	451			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978045	CTTTATCGATGTCCTCGGCC	TCGCCTTTATCGATGTCCTCGGCCAGGCCG	AGG	12	1841	2577	71.4		0.6250	0	0	RS3seq-Chen2013+RS3target	-0.7900	452			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977920	GATGTTGGGGCCGGTGCCGT	TGAGGATGTTGGGGCCGGTGCCGTCGGGCC	CGG	12	1966	2577	76.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.7970	453			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982324	GTGGCCAAGTTCGCCGCCAA	GTATGTGGCCAAGTTCGCCGCCAAGGGGGA	GGG	5	713	2577	27.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.8045	454			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976639	CCGCCACCACCTGGCTGGGG	GTCTCCGCCACCACCTGGCTGGGGCGGCTG	CGG	15	2492	2577	96.7		6.1337	0	0	RS3seq-Chen2013+RS3target	-0.8107	455			Aggregate CFD score > 4.80; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984322	CCTTCTTGTCCATGATGGCG	TTGGCCTTCTTGTCCATGATGGCGCGGATC	CGG	2	32	2577	1.2		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.8185	456			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3976637	CAACAGCAGCCGCCCCAGCC	TCGACAACAGCAGCCGCCCCAGCCAGGTGG	AGG	15	2494	2577	96.8		13.2656	2	0	RS3seq-Chen2013+RS3target	-0.8193	457			Aggregate CFD score > 4.80; Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978053	ATGTCCTCGGCCAGGCCGTC	ATCGATGTCCTCGGCCAGGCCGTCGGGGAA	GGG	12	1833	2577	71.1		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.8351	458			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977944	CCCAAAGCACCAGATCTTGC	CGGGCCCAAAGCACCAGATCTTGCGGGCCT	GGG	12	1942	2577	75.4		1.9089	0	0	RS3seq-Chen2013+RS3target	-0.8431	459			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982399	GTCCTCGGTACCGTGGGCTT	TCCTGTCCTCGGTACCGTGGGCTTTGGGTC	TGG	5	638	2577	24.8		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.8603	460			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 516
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977302	CGGGGTTTTGAACAGGAAGC	TCTACGGGGTTTTGAACAGGAAGCGGGGCC	GGG	14	2296	2577	89.1		1.4588	1	0	RS3seq-Chen2013+RS3target	-0.8617	461			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; poly(T):TTTT
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977465	TGGGCTCCATGAGGCGTGGC	TAGATGGGCTCCATGAGGCGTGGCTGGGCG	TGG	13	2213	2577	85.9		0.1176	0	0	RS3seq-Chen2013+RS3target	-0.8699	462			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977535	CTGGCCCCCTCCGCGGTGGA	TGATCTGGCCCCCTCCGCGGTGGATGGCGT	TGG	13	2143	2577	83.2		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.8739	463			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976656	GGGCGGCTGCTGTTGTCGAA	GCTGGGGCGGCTGCTGTTGTCGAAGGGGTC	GGG	15	2475	2577	96.0		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.8742	464			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3984215	CAAGGCGGGCATCATCGCCT	TGTGCAAGGCGGGCATCATCGCCTCGGCCC	CGG	2	139	2577	5.4		1.5430	1	1	RS3seq-Chen2013+RS3target	-0.8824	465			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980678	TTGGAAATATACATCATAAG	CATTTTGGAAATATACATCATAAGAGGGCC	AGG	9	1182	2577	45.9		0.6190	0	0	RS3seq-Chen2013+RS3target	-0.8833	466			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977898	CACCCTTGGTGATGTCGGTG	TGCACACCCTTGGTGATGTCGGTGAGGATG	AGG	12	1988	2577	77.1		0.2500	0	0	RS3seq-Chen2013+RS3target	-0.8981	467			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982283	CATCATGTCCTCTACTTTCT	TCTTCATCATGTCCTCTACTTTCTTGGCCC	TGG	5	754	2577	29.3		3.4605	0	0	RS3seq-Chen2013+RS3target	-0.9019	468			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982310	CGCCAAGGGGGAGGGCCAGT	TCGCCGCCAAGGGGGAGGGCCAGTTGGGGC	TGG	5	727	2577	28.2		1.4028	0	0	RS3seq-Chen2013+RS3target	-0.9043	469			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3981343	GCCCAGGCCGCACCTTCAGC	CTGGGCCCAGGCCGCACCTTCAGCAGGGGT	AGG	7	1007	2577	39.1		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.9097	470			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977263	GACCACAAACATGGGGGTGC	CCTTGACCACAAACATGGGGGTGCCGGCCA	CGG	14	2335	2577	90.6		9.4206	1	0	RS3seq-Chen2013+RS3target	-0.9106	471			Aggregate CFD score > 4.80; Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980648	AACCGACCTTTGTCGGAGGT	GTAGAACCGACCTTTGTCGGAGGTTGGCAC	TGG	9	1212	2577	47.0		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.9149	472			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3981377	GGACATCAAACTGGACAGCG	AACTGGACATCAAACTGGACAGCGAGGACA	AGG	7	973	2577	37.8		0.6667	0	0	RS3seq-Chen2013+RS3target	-0.9164	473			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 875, 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976576	ATTTGTCCAGGAAGTTGTCC	TACAATTTGTCCAGGAAGTTGTCCAGGGCA	AGG	15	2555	2577	99.1		0.5080	0	0	RS3seq-Chen2013+RS3target	-0.9449	474			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976708	ACACACACTGGGGGAACGCC	TCAAACACACACTGGGGGAACGCCTGGCCG	TGG	15	2423	2577	94.0		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.9591	475			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979891	CCCTGTTGTCAGAGTGGCCG	TCAGCCCTGTTGTCAGAGTGGCCGTGGAGG	TGG	10	1522	2577	59.1		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.9878	476			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3984205	ATCATCGCCTCGGCCCGGGC	GGGCATCATCGCCTCGGCCCGGGCCGGGGA	CGG	2	149	2577	5.8		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.015	477			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982006	CAGCTTCTTCCCTTCGGGGC	GTGGCAGCTTCTTCCCTTCGGGGCTGGTGG	TGG	6	838	2577	32.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.018	478			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984202	TGAAGCGTGTCTCCCCGGCC	TCAGTGAAGCGTGTCTCCCCGGCCCGGGCC	CGG	2	152	2577	5.9		0.9333	0	0	RS3seq-Chen2013+RS3target	-1.021	479			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982325	TGTGGCCAAGTTCGCCGCCA	TGTATGTGGCCAAGTTCGCCGCCAAGGGGG	AGG	5	712	2577	27.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.027	480			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980686	ATACATCATAAGAGGGCCTT	AAATATACATCATAAGAGGGCCTTTGGGGT	TGG	9	1174	2577	45.6		0.4667	0	0	RS3seq-Chen2013+RS3target	-1.032	481			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976597	GGGCAGGGATGCCTTCTTTC	TCCAGGGCAGGGATGCCTTCTTTCAGGCCC	AGG	15	2534	2577	98.3		5.0442	0	0	RS3seq-Chen2013+RS3target	-1.033	482			Aggregate CFD score > 4.80; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3977979	CCCACTCGTACTTCTCGGCC	ACGTCCCACTCGTACTTCTCGGCCAGGTAG	AGG	12	1907	2577	74.0		0.7895	0	0	RS3seq-Chen2013+RS3target	-1.042	483			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977287	GAAGCGGGGCCACGTGTTCG	ACAGGAAGCGGGGCCACGTGTTCGAGGAGT	AGG	14	2311	2577	89.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.084	484			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3982319	CAAGTTCGCCGCCAAGGGGG	TGGCCAAGTTCGCCGCCAAGGGGGAGGGCC	AGG	5	718	2577	27.9		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.099	485			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980611	CGCCTTTGGACGAGTCTTCT	TCTACGCCTTTGGACGAGTCTTCTCGGGGC	CGG	9	1249	2577	48.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.115	486			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980857	CCCATGGCAGCCTCGTCGTC	CGTACCCATGGCAGCCTCGTCGTCCGGGGG	CGG	8	1134	2577	44.0		0.4667	0	0	RS3seq-Chen2013+RS3target	-1.136	487			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980056	TTCCAGAACAATCTTGATGA	CTTCTTCCAGAACAATCTTGATGATGGGCC	TGG	10	1357	2577	52.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.199	488			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976655	GGGGCGGCTGCTGTTGTCGA	GGCTGGGGCGGCTGCTGTTGTCGAAGGGGT	AGG	15	2476	2577	96.1		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.205	489			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980873	CGTCCGGGGGCCCCTCGTAC	TCGTCGTCCGGGGGCCCCTCGTACAGGAGC	AGG	8	1118	2577	43.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.220	490			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982296	ACTTTCTTGGCCCGCTCGGC	CTCTACTTTCTTGGCCCGCTCGGCAGGCCC	AGG	5	741	2577	28.8		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.237	491			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978058	CTCGGCCAGGCCGTCGGGGA	TGTCCTCGGCCAGGCCGTCGGGGAAGGGCC	AGG	12	1828	2577	70.9		0.2857	0	0	RS3seq-Chen2013+RS3target	-1.251	492			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976581	TCCAGGAAGTTGTCCAGGGC	TTTGTCCAGGAAGTTGTCCAGGGCAGGGAT	AGG	15	2550	2577	99.0		2.3635	0	0	RS3seq-Chen2013+RS3target	-1.255	493			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982331	CTCCCCCTTGGCGGCGAACT	GGCCCTCCCCCTTGGCGGCGAACTTGGCCA	TGG	5	706	2577	27.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.263	494			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982009	CTTCTTCCCTTCGGGGCTGG	GCAGCTTCTTCCCTTCGGGGCTGGTGGCTG	TGG	6	835	2577	32.4		2.5564	0	0	RS3seq-Chen2013+RS3target	-1.263	495			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977933	CCCGCAAGATCTGGTGCTTT	GAGGCCCGCAAGATCTGGTGCTTTGGGCCC	GGG	12	1953	2577	75.8		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.269	496			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3978103	GTTGTGCTTGTTGGGGGACT	GCCGGTTGTGCTTGTTGGGGGACTTGGAGA	TGG	12	1783	2577	69.2		1.6303	1	0	RS3seq-Chen2013+RS3target	-1.295	497			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3980865	GCTCCTGTACGAGGGGCCCC	GCGAGCTCCTGTACGAGGGGCCCCCGGACG	CGG	8	1126	2577	43.7		1.0000	1	0	RS3seq-Chen2013+RS3target	-1.322	498			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3979869	CCAGCTTGGGCAGGTCAGCC	TCCACCAGCTTGGGCAGGTCAGCCGGGTTC	GGG	10	1544	2577	59.9		1.1144	0	0	RS3seq-Chen2013+RS3target	-1.322	499			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976634	GGTCTCCGCCACCACCTGGC	TGCGGGTCTCCGCCACCACCTGGCTGGGGC	TGG	15	2497	2577	96.9		0.1389	0	0	RS3seq-Chen2013+RS3target	-1.356	500			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3976571	CCCTGCCCTGGACAACTTCC	GCATCCCTGCCCTGGACAACTTCCTGGACA	TGG	15	2560	2577	99.3		3.1299	1	0	RS3seq-Chen2013+RS3target	-1.382	501			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3976715	GAGGTCCAACACGGGCGGCC	ACCTGAGGTCCAACACGGGCGGCCAGGCGT	AGG	15	2416	2577	93.8		0.8667	0	0	RS3seq-Chen2013+RS3target	-1.400	502			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3978046	CTTCCCCGACGGCCTGGCCG	GGCCCTTCCCCGACGGCCTGGCCGAGGACA	AGG	12	1840	2577	71.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.402	503			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3984204	TCATCGCCTCGGCCCGGGCC	GGCATCATCGCCTCGGCCCGGGCCGGGGAG	GGG	2	150	2577	5.8		0.6190	0	0	RS3seq-Chen2013+RS3target	-1.411	504			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3980710	GTCACAGCTTTTAATGCCTG	TGGGGTCACAGCTTTTAATGCCTGAGGGAC	AGG	9	1150	2577	44.6		0.4286	0	0	RS3seq-Chen2013+RS3target	-1.421	505			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1051; poly(T):TTTT
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3976628	CCGCCCCAGCCAGGTGGTGG	GCAGCCGCCCCAGCCAGGTGGTGGCGGAGA	CGG	15	2503	2577	97.1		7.0931	0	0	RS3seq-Chen2013+RS3target	-1.467	506			Aggregate CFD score > 4.80; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982292	CTCTACTTTCTTGGCCCGCT	TGTCCTCTACTTTCTTGGCCCGCTCGGCAG	CGG	5	745	2577	28.9		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.508	507			On-Target Efficacy Score < 0.2
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982038	TGCTGAACTTGCCGTTGGCT	GACTTGCTGAACTTGCCGTTGGCTGGGTCA	GGG	6	806	2577	31.3		0.7000	0	0	RS3seq-Chen2013+RS3target	-1.566	508			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3984203	CATCGCCTCGGCCCGGGCCG	GCATCATCGCCTCGGCCCGGGCCGGGGAGA	GGG	2	151	2577	5.9		1.1561	0	0	RS3seq-Chen2013+RS3target	-1.643	509			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3984209	TGTCTCCCCGGCCCGGGCCG	AGCGTGTCTCCCCGGCCCGGGCCGAGGCGA	AGG	2	145	2577	5.6		2.7619	1	0	RS3seq-Chen2013+RS3target	-1.645	510			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3977248	CACCCCCATGTTTGTGGTCA	CCGGCACCCCCATGTTTGTGGTCAAGGCCT	AGG	14	2350	2577	91.2		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.665	511			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976613	TTTCAGGCCCTTGCGCTTGC	CTTCTTTCAGGCCCTTGCGCTTGCGGGTCT	GGG	15	2518	2577	97.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.754	512			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3979993	CATTGTGGGCCTCGTGGGCG	GGAACATTGTGGGCCTCGTGGGCGTGGACC	TGG	10	1420	2577	55.1		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.758	513			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 1346
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3984210	CGGGCATCATCGCCTCGGCC	AAGGCGGGCATCATCGCCTCGGCCCGGGCC	CGG	2	144	2577	5.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.795	514			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 226
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	-	sense	3978052	GCGGCCCTTCCCCGACGGCC	AGGCGCGGCCCTTCCCCGACGGCCTGGCCG	TGG	12	1834	2577	71.2		1.2161	0	0	RS3seq-Chen2013+RS3target	-1.825	515			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3982037	TTGCTGAACTTGCCGTTGGC	TGACTTGCTGAACTTGCCGTTGGCTGGGTC	TGG	6	807	2577	31.3		0.9375	0	0	RS3seq-Chen2013+RS3target	-1.840	516			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 875
+EEF2	5	9606	1938	EEF2	NM_001961.4	EEF2	CRISPRko	CDS	NC_000019.10	-	SpyoCas9	5	1	1	+	antisense	3976612	CTTTCAGGCCCTTGCGCTTG	CCTTCTTTCAGGCCCTTGCGCTTGCGGGTC	CGG	15	2519	2577	97.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-2.187	517			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721744	AAGCGTAGACAAGGAGATGC	TGGAAAGCGTAGACAAGGAGATGCAGGTAT	AGG	2	475	702	67.7	103021294 (ABALON)	1.0000	1	0	RS3seq-Chen2013+RS3target	1.273	1			Off-target CFD100 matches > 0; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721849	AGCATATCAGAGCTTTGAAC	GGACAGCATATCAGAGCTTTGAACAGGTAG	AGG	2	370	702	52.7	103021294 (ABALON)	2.0091	0	0	RS3seq-Chen2013+RS3target	0.9903	2	1	1	
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721821	GACCCCAGTTTACCCCATCC	ATGCGACCCCAGTTTACCCCATCCCGGAAG	CGG	2	398	702	56.7	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	0.9828	3			Spacing Violation: Too close to earlier pick at position 370
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721875	TCCCAGCTCCACATCACCCC	GACATCCCAGCTCCACATCACCCCAGGGAC	AGG	2	344	702	49.0	103021294 (ABALON)	1.4588	1	0	RS3seq-Chen2013+RS3target	0.9416	4			Off-target CFD100 matches > 0; Spacing Violation: Too close to earlier pick at position 370
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721753	GTGCGTGGAAAGCGTAGACA	CACTGTGCGTGGAAAGCGTAGACAAGGAGA	AGG	2	466	702	66.4	103021294 (ABALON)	0.1905	0	0	RS3seq-Chen2013+RS3target	0.8798	5			Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721975	TGCCCGGGAGGTGATCCCCA	TGGATGCCCGGGAGGTGATCCCCATGGCAG	TGG	2	244	702	34.8	103021294 (ABALON)	1.7458	0	0	RS3seq-Chen2013+RS3target	0.8523	6	2	1	
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721829	AGGTAGTGAATGAACTCTTC	GAACAGGTAGTGAATGAACTCTTCCGGGAT	CGG	2	390	702	55.6	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	0.8119	7			Spacing Violation: Too close to earlier pick at position 370
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721911	TCAGGTCACTGAATGCCCGC	GATGTCAGGTCACTGAATGCCCGCCGGTAC	CGG	2	308	702	43.9	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	0.7888	8	3	1	
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721683	TCTCCTGGATCCAAGGCTCT	CCGTTCTCCTGGATCCAAGGCTCTAGGTGG	AGG	2	536	702	76.4	103021294 (ABALON)	1.3820	0	0	RS3seq-Chen2013+RS3target	0.7027	9			BsmBI:5'cgTCTC; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721812	TTCCGGGATGGGGTAAACTG	ACTCTTCCGGGATGGGGTAAACTGGGGTCG	GGG	2	407	702	58.0	103021294 (ABALON)	0.1765	0	0	RS3seq-Chen2013+RS3target	0.6896	10	4	1	
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721925	CAGGCGACGAGTTTGAACTG	GAGGCAGGCGACGAGTTTGAACTGCGGTAC	CGG	2	294	702	41.9	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	0.6872	11			Spacing Violation: Too close to earlier pick at position 308
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721714	GAGTCGGATCGCAGCTTGGA	TGGTGAGTCGGATCGCAGCTTGGATGGCCA	TGG	2	505	702	71.9	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	0.6345	12			Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721695	AAGGCTCTAGGTGGTCATTC	ATCCAAGGCTCTAGGTGGTCATTCAGGTAA	AGG	2	524	702	74.6	103021294 (ABALON)	0.5789	0	0	RS3seq-Chen2013+RS3target	0.6331	13			Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721874	CCCAGCTCCACATCACCCCA	ACATCCCAGCTCCACATCACCCCAGGGACA	GGG	2	345	702	49.1	103021294 (ABALON)	0.4667	0	0	RS3seq-Chen2013+RS3target	0.6318	14			Spacing Violation: Too close to earlier pick at position 370
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721738	AGACAAGGAGATGCAGGTAT	GCGTAGACAAGGAGATGCAGGTATTGGTGA	TGG	2	481	702	68.5	103021294 (ABALON)	1.6521	0	0	RS3seq-Chen2013+RS3target	0.6088	15			Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721952	CAGCAGTAAAGCAAGCGCTG	ATGGCAGCAGTAAAGCAAGCGCTGAGGGAG	AGG	2	267	702	38.0	103021294 (ABALON)	1.8199	0	0	RS3seq-Chen2013+RS3target	0.5832	16			Spacing Violation: Too close to earlier pick at position 244
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721870	GCTCTGATATGCTGTCCCTG	CAAAGCTCTGATATGCTGTCCCTGGGGTGA	GGG	2	349	702	49.7	103021294 (ABALON)	1.4256	0	0	RS3seq-Chen2013+RS3target	0.5528	17			Spacing Violation: Too close to earlier pick at position 370
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721665	CCTTGGATCCAGGAGAACGG	AGAGCCTTGGATCCAGGAGAACGGCGGCTG	CGG	2	554	702	78.9	103021294 (ABALON)	0.1429	0	0	RS3seq-Chen2013+RS3target	0.5513	18			Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721702	TAGGTGGTCATTCAGGTAAG	GCTCTAGGTGGTCATTCAGGTAAGTGGCCA	TGG	2	517	702	73.6	103021294 (ABALON)	1.7294	0	0	RS3seq-Chen2013+RS3target	0.5445	19			Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31722197	TCAGAGCAACCGGGAGCTGG	TGTCTCAGAGCAACCGGGAGCTGGTGGTTG	TGG	2	22	702	3.1	103021294 (ABALON)	3.2703	1	0	RS3seq-Chen2013+RS3target	0.5312	20			Off-target CFD100 matches > 0; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31666013	TCATGCCCGTCAGGAACCAG	ACAGTCATGCCCGTCAGGAACCAGCGGTTG	CGG	3	638	702	90.9		1.4000	0	0	RS3seq-Chen2013+RS3target	0.5160	21			Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722199	GAAAGTCAACCACCAGCTCC	GAGAGAAAGTCAACCACCAGCTCCCGGTTG	CGG	2	20	702	2.8	103021294 (ABALON)	0.9255	0	0	RS3seq-Chen2013+RS3target	0.5025	22			Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721780	CACGCACAGTGCCCCGCCGA	TTTCCACGCACAGTGCCCCGCCGAAGGAGA	AGG	2	439	702	62.5	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	0.5020	23			Spacing Violation: Too close to earlier pick at position 407
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721730	AGATGCAGGTATTGGTGAGT	AAGGAGATGCAGGTATTGGTGAGTCGGATC	CGG	2	489	702	69.7	103021294 (ABALON)	2.8000	0	0	RS3seq-Chen2013+RS3target	0.4943	24			Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721869	AGCTCTGATATGCTGTCCCT	TCAAAGCTCTGATATGCTGTCCCTGGGGTG	GGG	2	350	702	49.9	103021294 (ABALON)	1.0000	1	0	RS3seq-Chen2013+RS3target	0.4765	25			Off-target CFD100 matches > 0; Spacing Violation: Too close to earlier pick at position 370
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721668	GAGCCTTGGATCCAGGAGAA	CCTAGAGCCTTGGATCCAGGAGAACGGCGG	CGG	2	551	702	78.5	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	0.4491	26			Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31722105	ACAGGACTGAGGCCCCAGAA	GAGAACAGGACTGAGGCCCCAGAAGGGACT	GGG	2	114	702	16.2	103021294 (ABALON)	3.3718	0	0	RS3seq-Chen2013+RS3target	0.4300	27	5	1	
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31722123	TTAGTGATGTGGAAGAGAAC	CAGTTTAGTGATGTGGAAGAGAACAGGACT	AGG	2	96	702	13.7	103021294 (ABALON)	0.8331	0	0	RS3seq-Chen2013+RS3target	0.4259	28			Spacing Violation: Too close to earlier pick at position 114
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31722095	GGCCCCAGAAGGGACTGAAT	CTGAGGCCCCAGAAGGGACTGAATCGGAGA	CGG	2	124	702	17.7	103021294 (ABALON)	0.9333	0	0	RS3seq-Chen2013+RS3target	0.3806	29			Spacing Violation: Too close to earlier pick at position 114
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31722206	AAAAATGTCTCAGAGCAACC	TTATAAAAATGTCTCAGAGCAACCGGGAGC	GGG	2	13	702	1.9	103021294 (ABALON)	0.0588	0	0	RS3seq-Chen2013+RS3target	0.3730	30			Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31722067	GAGACCCCCAGTGCCATCAA	GATGGAGACCCCCAGTGCCATCAATGGCAA	TGG	2	152	702	21.7	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	0.3558	31			Quota Met
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721948	AGTAAAGCAAGCGCTGAGGG	CAGCAGTAAAGCAAGCGCTGAGGGAGGCAG	AGG	2	271	702	38.6	103021294 (ABALON)	2.1429	0	0	RS3seq-Chen2013+RS3target	0.3401	32			Spacing Violation: Too close to earlier pick at position 244
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721868	AAGCTCTGATATGCTGTCCC	TTCAAAGCTCTGATATGCTGTCCCTGGGGT	TGG	2	351	702	50.0	103021294 (ABALON)	1.2265	0	0	RS3seq-Chen2013+RS3target	0.2919	33			Spacing Violation: Too close to earlier pick at position 370
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31722032	GCACCTGGCAGACAGCCCCG	CCTGGCACCTGGCAGACAGCCCCGCGGTGA	CGG	2	187	702	26.6	103021294 (ABALON)	0.2632	0	0	RS3seq-Chen2013+RS3target	0.2830	34			Quota Met
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721828	GGTAGTGAATGAACTCTTCC	AACAGGTAGTGAATGAACTCTTCCGGGATG	GGG	2	391	702	55.7	103021294 (ABALON)	0.8667	0	0	RS3seq-Chen2013+RS3target	0.2641	35			Spacing Violation: Too close to earlier picks at positions 370, 407
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31722047	TGGCAACCCATCCTGGCACC	TCAATGGCAACCCATCCTGGCACCTGGCAG	TGG	2	172	702	24.5	103021294 (ABALON)	1.1688	0	0	RS3seq-Chen2013+RS3target	0.2317	36			Quota Met
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721878	ATGCTGTCCCTGGGGTGATG	TGATATGCTGTCCCTGGGGTGATGTGGAGC	TGG	2	341	702	48.6	103021294 (ABALON)	5.8659	0	0	RS3seq-Chen2013+RS3target	0.2237	37			Aggregate CFD score > 4.80; Spacing Violation: Too close to earlier picks at positions 308, 370
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722052	TGTCTGCCAGGTGCCAGGAT	GGGCTGTCTGCCAGGTGCCAGGATGGGTTG	GGG	2	167	702	23.8	103021294 (ABALON)	1.4098	0	0	RS3seq-Chen2013+RS3target	0.2120	38			Quota Met
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31665984	TGAGCCCAGCAGAACCACGC	AGAGTGAGCCCAGCAGAACCACGCCGGCCA	CGG	3	667	702	95.0		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2077	39			Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31666007	TTCAACCGCTGGTTCCTGAC	ACGCTTCAACCGCTGGTTCCTGACGGGCAT	GGG	3	644	702	91.7		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2020	40			Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721990	CAGCAGCAGTTTGGATGCCC	GCCACAGCAGCAGTTTGGATGCCCGGGAGG	GGG	2	229	702	32.6	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	0.2020	41			Spacing Violation: Too close to earlier pick at position 244
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31666040	GCAGCAGCCGAGAGCCGAAA	CAATGCAGCAGCCGAGAGCCGAAAGGGCCA	GGG	3	611	702	87.0		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2017	42			Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721668	TTACCCAGCCGCCGTTCTCC	GTTCTTACCCAGCCGCCGTTCTCCTGGATC	TGG	2	551	702	78.5	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	0.1771	43			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31666018	GCCAGGAACGCTTCAACCGC	AAGGGCCAGGAACGCTTCAACCGCTGGTTC	TGG	3	633	702	90.2		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1089	44			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721676	CCGCCGTTCTCCTGGATCCA	CCAGCCGCCGTTCTCCTGGATCCAAGGCTC	AGG	2	543	702	77.4	103021294 (ABALON)	1.1048	0	0	RS3seq-Chen2013+RS3target	0.1015	45			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31722200	GTCTCAGAGCAACCGGGAGC	AAATGTCTCAGAGCAACCGGGAGCTGGTGG	TGG	2	19	702	2.7	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	0.1010	46			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721893	TGATGTGGAGCTGGGATGTC	GGGGTGATGTGGAGCTGGGATGTCAGGTCA	AGG	2	326	702	46.4	103021294 (ABALON)	1.5126	0	0	RS3seq-Chen2013+RS3target	0.09431	47			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 308
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721801	GGTAAACTGGGGTCGCATTG	ATGGGGTAAACTGGGGTCGCATTGTGGCCT	TGG	2	418	702	59.5	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	0.09313	48			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 407
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721824	GTGAATGAACTCTTCCGGGA	GGTAGTGAATGAACTCTTCCGGGATGGGGT	TGG	2	395	702	56.3	103021294 (ABALON)	1.3077	1	0	RS3seq-Chen2013+RS3target	0.07202	49			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 370, 407
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721915	GTTTGAACTGCGGTACCGGC	ACGAGTTTGAACTGCGGTACCGGCGGGCAT	GGG	2	304	702	43.3	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	0.07037	50			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 308
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722173	TCCTTTCTGGGAAAGCTTGT	TGTATCCTTTCTGGGAAAGCTTGTAGGAGA	AGG	2	46	702	6.6	103021294 (ABALON)	1.0614	0	0	RS3seq-Chen2013+RS3target	0.06256	51			On-Target Efficacy Score < 0.2
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721919	ACGAGTTTGAACTGCGGTAC	GGCGACGAGTTTGAACTGCGGTACCGGCGG	CGG	2	300	702	42.7	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	0.05121	52			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 308
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31666041	TGCAGCAGCCGAGAGCCGAA	ACAATGCAGCAGCCGAGAGCCGAAAGGGCC	AGG	3	610	702	86.9		0.0000	0	0	RS3seq-Chen2013+RS3target	0.04232	53			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721951	AGCAGTAAAGCAAGCGCTGA	TGGCAGCAGTAAAGCAAGCGCTGAGGGAGG	GGG	2	268	702	38.2	103021294 (ABALON)	0.1333	0	0	RS3seq-Chen2013+RS3target	0.02204	54			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 244
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31722016	CCCGCGGTGAATGGAGCCAC	CAGCCCCGCGGTGAATGGAGCCACTGGCCA	TGG	2	203	702	28.9	103021294 (ABALON)	1.6932	0	0	RS3seq-Chen2013+RS3target	0.01376	55			On-Target Efficacy Score < 0.2
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721822	GAATGAACTCTTCCGGGATG	TAGTGAATGAACTCTTCCGGGATGGGGTAA	GGG	2	397	702	56.6	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-0.01832	56			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 370, 407
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721686	CCTGGATCCAAGGCTCTAGG	TTCTCCTGGATCCAAGGCTCTAGGTGGTCA	TGG	2	533	702	75.9	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-0.02056	57			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721682	TGAATGACCACCTAGAGCCT	TACCTGAATGACCACCTAGAGCCTTGGATC	TGG	2	537	702	76.5	103021294 (ABALON)	0.7167	0	0	RS3seq-Chen2013+RS3target	-0.02406	58			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31722134	CTGGAGTCAGTTTAGTGATG	ACAGCTGGAGTCAGTTTAGTGATGTGGAAG	TGG	2	85	702	12.1	103021294 (ABALON)	1.7599	0	0	RS3seq-Chen2013+RS3target	-0.02555	59			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 114
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31722089	AGAAGGGACTGAATCGGAGA	CCCCAGAAGGGACTGAATCGGAGATGGAGA	TGG	2	130	702	18.5	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-0.02657	60			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 114
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721916	AGTTTGAACTGCGGTACCGG	GACGAGTTTGAACTGCGGTACCGGCGGGCA	CGG	2	303	702	43.2	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-0.03896	61			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 308
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722028	CAGTGGCTCCATTCACCGCG	TGGCCAGTGGCTCCATTCACCGCGGGGCTG	GGG	2	191	702	27.2	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-0.04838	62			On-Target Efficacy Score < 0.2
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31722153	TTTCCCAGAAAGGATACAGC	AAGCTTTCCCAGAAAGGATACAGCTGGAGT	TGG	2	66	702	9.4	103021294 (ABALON)	0.7210	0	0	RS3seq-Chen2013+RS3target	-0.09093	63			On-Target Efficacy Score < 0.2
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31665996	GTTCCTGACGGGCATGACTG	GCTGGTTCCTGACGGGCATGACTGTGGCCG	TGG	3	655	702	93.3		0.2593	0	0	RS3seq-Chen2013+RS3target	-0.1416	64			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31722116	TGTGGAAGAGAACAGGACTG	GTGATGTGGAAGAGAACAGGACTGAGGCCC	AGG	2	103	702	14.7	103021294 (ABALON)	2.0030	0	0	RS3seq-Chen2013+RS3target	-0.1606	65			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 114
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721660	GATCCAGGAGAACGGCGGCT	CTTGGATCCAGGAGAACGGCGGCTGGGTAA	GGG	2	559	702	79.6	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1734	66			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721885	CCCTGGGGTGATGTGGAGCT	CTGTCCCTGGGGTGATGTGGAGCTGGGATG	GGG	2	334	702	47.6	103021294 (ABALON)	2.3880	1	0	RS3seq-Chen2013+RS3target	-0.2086	67			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 308
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721999	CACTGGCCACAGCAGCAGTT	GAGCCACTGGCCACAGCAGCAGTTTGGATG	TGG	2	220	702	31.3	103021294 (ABALON)	3.1566	2	0	RS3seq-Chen2013+RS3target	-0.2112	68			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 244
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31665978	TGTGGCCGGCGTGGTTCTGC	TGACTGTGGCCGGCGTGGTTCTGCTGGGCT	TGG	3	673	702	95.9		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2154	69			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721984	TGCCATGGGGATCACCTCCC	CTGCTGCCATGGGGATCACCTCCCGGGCAT	GGG	2	235	702	33.5	103021294 (ABALON)	0.6429	0	0	RS3seq-Chen2013+RS3target	-0.2155	70			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 244
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722026	GCCAGTGGCTCCATTCACCG	TGTGGCCAGTGGCTCCATTCACCGCGGGGC	CGG	2	193	702	27.5	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2177	71			On-Target Efficacy Score < 0.2
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722027	CCAGTGGCTCCATTCACCGC	GTGGCCAGTGGCTCCATTCACCGCGGGGCT	GGG	2	192	702	27.4	103021294 (ABALON)	0.1333	0	0	RS3seq-Chen2013+RS3target	-0.2245	72			On-Target Efficacy Score < 0.2
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722161	ACTCCAGCTGTATCCTTTCT	ACTGACTCCAGCTGTATCCTTTCTGGGAAA	GGG	2	58	702	8.3	103021294 (ABALON)	1.7417	0	0	RS3seq-Chen2013+RS3target	-0.2285	73			On-Target Efficacy Score < 0.2
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722074	GTTGCCATTGATGGCACTGG	ATGGGTTGCCATTGATGGCACTGGGGGTCT	GGG	2	145	702	20.7	103021294 (ABALON)	0.2593	0	0	RS3seq-Chen2013+RS3target	-0.2612	74			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 114
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721675	CCACCTAGAGCCTTGGATCC	ATGACCACCTAGAGCCTTGGATCCAGGAGA	AGG	2	544	702	77.5	103021294 (ABALON)	0.3333	0	0	RS3seq-Chen2013+RS3target	-0.2641	75			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721944	AAGCAAGCGCTGAGGGAGGC	AGTAAAGCAAGCGCTGAGGGAGGCAGGCGA	AGG	2	275	702	39.2	103021294 (ABALON)	1.6813	0	0	RS3seq-Chen2013+RS3target	-0.2655	76			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 244, 308
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721789	TGCCCCGCCGAAGGAGAAAA	ACAGTGCCCCGCCGAAGGAGAAAAAGGCCA	AGG	2	430	702	61.3	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2688	77			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 407
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721971	CTTGCTTTACTGCTGCCATG	AGCGCTTGCTTTACTGCTGCCATGGGGATC	GGG	2	248	702	35.3	103021294 (ABALON)	0.6667	0	0	RS3seq-Chen2013+RS3target	-0.2914	78			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 244
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721987	CAGCAGTTTGGATGCCCGGG	ACAGCAGCAGTTTGGATGCCCGGGAGGTGA	AGG	2	232	702	33.0	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2915	79			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 244
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31722106	AACAGGACTGAGGCCCCAGA	AGAGAACAGGACTGAGGCCCCAGAAGGGAC	AGG	2	113	702	16.1	103021294 (ABALON)	6.2615	1	0	RS3seq-Chen2013+RS3target	-0.2917	80			Aggregate CFD score > 4.80; Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 114
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31666028	ACCAGCGGTTGAAGCGTTCC	AGGAACCAGCGGTTGAAGCGTTCCTGGCCC	TGG	3	623	702	88.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3096	81			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721814	TCTTCCGGGATGGGGTAAAC	GAACTCTTCCGGGATGGGGTAAACTGGGGT	TGG	2	405	702	57.7	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3550	82			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 370, 407
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722004	GGGCATCCAAACTGCTGCTG	TCCCGGGCATCCAAACTGCTGCTGTGGCCA	TGG	2	215	702	30.6	103021294 (ABALON)	1.1684	0	0	RS3seq-Chen2013+RS3target	-0.3650	83			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 244
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31722025	GCAGACAGCCCCGCGGTGAA	CCTGGCAGACAGCCCCGCGGTGAATGGAGC	TGG	2	194	702	27.6	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3838	84			On-Target Efficacy Score < 0.2
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721884	TCCCTGGGGTGATGTGGAGC	GCTGTCCCTGGGGTGATGTGGAGCTGGGAT	TGG	2	335	702	47.7	103021294 (ABALON)	2.4081	0	0	RS3seq-Chen2013+RS3target	-0.3912	85			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 308, 370
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722073	GGTTGCCATTGATGGCACTG	GATGGGTTGCCATTGATGGCACTGGGGGTC	GGG	2	146	702	20.8	103021294 (ABALON)	1.0222	1	0	RS3seq-Chen2013+RS3target	-0.4270	86			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 114
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721768	CTTCGGCGGGGCACTGTGCG	TCTCCTTCGGCGGGGCACTGTGCGTGGAAA	TGG	2	451	702	64.2	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4296	87			On-Target Efficacy Score < 0.2
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722065	CCAGGATGGGTTGCCATTGA	GGTGCCAGGATGGGTTGCCATTGATGGCAC	TGG	2	154	702	21.9	103021294 (ABALON)	0.6667	0	0	RS3seq-Chen2013+RS3target	-0.4680	88			On-Target Efficacy Score < 0.2
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722011	CAAACTGCTGCTGTGGCCAG	CATCCAAACTGCTGCTGTGGCCAGTGGCTC	TGG	2	208	702	29.6	103021294 (ABALON)	3.6385	0	0	RS3seq-Chen2013+RS3target	-0.4701	89			On-Target Efficacy Score < 0.2
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31665977	GTGGCCGGCGTGGTTCTGCT	GACTGTGGCCGGCGTGGTTCTGCTGGGCTC	GGG	3	674	702	96.0		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4814	90			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721813	CTTCCGGGATGGGGTAAACT	AACTCTTCCGGGATGGGGTAAACTGGGGTC	GGG	2	406	702	57.8	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4879	91			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 407
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721718	TGGTGAGTCGGATCGCAGCT	GTATTGGTGAGTCGGATCGCAGCTTGGATG	TGG	2	501	702	71.4	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4944	92			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31665961	TGCTGGGCTCACTCTTCAGT	GTTCTGCTGGGCTCACTCTTCAGTCGGAAA	CGG	3	690	702	98.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.5004	93			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722051	CTGTCTGCCAGGTGCCAGGA	GGGGCTGTCTGCCAGGTGCCAGGATGGGTT	TGG	2	168	702	23.9	103021294 (ABALON)	1.5915	0	0	RS3seq-Chen2013+RS3target	-0.5340	94			On-Target Efficacy Score < 0.2
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721983	CTGCCATGGGGATCACCTCC	ACTGCTGCCATGGGGATCACCTCCCGGGCA	CGG	2	236	702	33.6	103021294 (ABALON)	0.7275	0	0	RS3seq-Chen2013+RS3target	-0.5463	95			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 244
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31666004	CGGCCACAGTCATGCCCGTC	ACGCCGGCCACAGTCATGCCCGTCAGGAAC	AGG	3	647	702	92.2		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.5476	96			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31666008	CTTCAACCGCTGGTTCCTGA	AACGCTTCAACCGCTGGTTCCTGACGGGCA	CGG	3	643	702	91.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.6110	97			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721823	TGAATGAACTCTTCCGGGAT	GTAGTGAATGAACTCTTCCGGGATGGGGTA	GGG	2	396	702	56.4	103021294 (ABALON)	0.2222	0	0	RS3seq-Chen2013+RS3target	-0.6220	98			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 370, 407
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722160	GACTCCAGCTGTATCCTTTC	AACTGACTCCAGCTGTATCCTTTCTGGGAA	TGG	2	59	702	8.4	103021294 (ABALON)	0.6522	0	0	RS3seq-Chen2013+RS3target	-0.6381	99			On-Target Efficacy Score < 0.2
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31666069	ATACTTTTGTGGAACTCTAT	CAGGATACTTTTGTGGAACTCTATGGGAAC	GGG	3	582	702	82.9		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.6505	100			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; poly(T):TTTT
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722102	ATCTCCGATTCAGTCCCTTC	CTCCATCTCCGATTCAGTCCCTTCTGGGGC	TGG	2	117	702	16.7	103021294 (ABALON)	0.6875	0	0	RS3seq-Chen2013+RS3target	-0.6548	101			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 114
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31722163	TCCTACAAGCTTTCCCAGAA	TCTCTCCTACAAGCTTTCCCAGAAAGGATA	AGG	2	56	702	8.0	103021294 (ABALON)	0.2593	0	0	RS3seq-Chen2013+RS3target	-0.6664	102			On-Target Efficacy Score < 0.2
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31665992	CTGACGGGCATGACTGTGGC	GTTCCTGACGGGCATGACTGTGGCCGGCGT	CGG	3	659	702	93.9		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.6880	103			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722104	CTCCGATTCAGTCCCTTCTG	CCATCTCCGATTCAGTCCCTTCTGGGGCCT	GGG	2	115	702	16.4	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-0.7066	104			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 114
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722071	TGGGTTGCCATTGATGGCAC	AGGATGGGTTGCCATTGATGGCACTGGGGG	TGG	2	148	702	21.1	103021294 (ABALON)	0.2143	0	0	RS3seq-Chen2013+RS3target	-0.7089	105			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 114
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722103	TCTCCGATTCAGTCCCTTCT	TCCATCTCCGATTCAGTCCCTTCTGGGGCC	GGG	2	116	702	16.5	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-0.7092	106			BsmBI:5'cgTCTC; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 114
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31722054	CCATCAATGGCAACCCATCC	AGTGCCATCAATGGCAACCCATCCTGGCAC	TGG	2	165	702	23.5	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-0.7940	107			On-Target Efficacy Score < 0.2
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721661	GGATCCAGGAGAACGGCGGC	CCTTGGATCCAGGAGAACGGCGGCTGGGTA	TGG	2	558	702	79.5	103021294 (ABALON)	0.6250	0	0	RS3seq-Chen2013+RS3target	-0.8006	108			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722072	GGGTTGCCATTGATGGCACT	GGATGGGTTGCCATTGATGGCACTGGGGGT	GGG	2	147	702	20.9	103021294 (ABALON)	0.7159	0	0	RS3seq-Chen2013+RS3target	-0.8257	109			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 114
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721970	GCTTGCTTTACTGCTGCCAT	CAGCGCTTGCTTTACTGCTGCCATGGGGAT	GGG	2	249	702	35.5	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-0.8624	110			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 244
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31666037	TGAAGCGTTCCTGGCCCTTT	CGGTTGAAGCGTTCCTGGCCCTTTCGGCTC	CGG	3	614	702	87.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.9668	111			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721991	ACAGCAGCAGTTTGGATGCC	GGCCACAGCAGCAGTTTGGATGCCCGGGAG	CGG	2	228	702	32.5	103021294 (ABALON)	0.6667	0	0	RS3seq-Chen2013+RS3target	-0.9865	112			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 244
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31666044	TTCCTGGCCCTTTCGGCTCT	AGCGTTCCTGGCCCTTTCGGCTCTCGGCTG	CGG	3	607	702	86.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.9885	113			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721780	GGCCTTTTTCTCCTTCGGCG	TTGTGGCCTTTTTCTCCTTCGGCGGGGCAC	GGG	2	439	702	62.5	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-0.9960	114			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 407; poly(T):TTTTT
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31722207	TAAAAATGTCTCAGAGCAAC	ATTATAAAAATGTCTCAGAGCAACCGGGAG	CGG	2	12	702	1.7	103021294 (ABALON)	1.5917	0	0	RS3seq-Chen2013+RS3target	-1.023	115			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31666035	AGCCGAGAGCCGAAAGGGCC	CAGCAGCCGAGAGCCGAAAGGGCCAGGAAC	AGG	3	616	702	87.7		0.2500	0	0	RS3seq-Chen2013+RS3target	-1.090	116			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722040	TCACCGCGGGGCTGTCTGCC	CCATTCACCGCGGGGCTGTCTGCCAGGTGC	AGG	2	179	702	25.5	103021294 (ABALON)	0.9048	0	0	RS3seq-Chen2013+RS3target	-1.119	117			On-Target Efficacy Score < 0.2
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721782	GTGGCCTTTTTCTCCTTCGG	CATTGTGGCCTTTTTCTCCTTCGGCGGGGC	CGG	2	437	702	62.3	103021294 (ABALON)	0.0000	0	0	RS3seq-Chen2013+RS3target	-1.127	118			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 407; poly(T):TTTTT
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31665987	GGGCATGACTGTGGCCGGCG	TGACGGGCATGACTGTGGCCGGCGTGGTTC	TGG	3	664	702	94.6		0.8571	0	0	RS3seq-Chen2013+RS3target	-1.251	119			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31666080	CTCCCTGCAGGATACTTTTG	CTCTCTCCCTGCAGGATACTTTTGTGGAAC	TGG	3	571	702	81.3		0.2857	0	0	RS3seq-Chen2013+RS3target	-1.402	120			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; poly(T):TTTT
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721785	ATTGTGGCCTTTTTCTCCTT	TCGCATTGTGGCCTTTTTCTCCTTCGGCGG	CGG	2	434	702	61.8	103021294 (ABALON)	1.0000	1	0	RS3seq-Chen2013+RS3target	-1.419	121			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 407; poly(T):TTTTT
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31666070	GATACTTTTGTGGAACTCTA	GCAGGATACTTTTGTGGAACTCTATGGGAA	TGG	3	581	702	82.8		2.2333	1	0	RS3seq-Chen2013+RS3target	-1.483	122			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; poly(T):TTTT
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	-	sense	31721781	TGGCCTTTTTCTCCTTCGGC	ATTGTGGCCTTTTTCTCCTTCGGCGGGGCA	GGG	2	438	702	62.4	103021294 (ABALON)	0.9333	0	0	RS3seq-Chen2013+RS3target	-1.484	123			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 407; poly(T):TTTTT
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31722047	GGGGCTGTCTGCCAGGTGCC	CCGCGGGGCTGTCTGCCAGGTGCCAGGATG	AGG	2	172	702	24.5	103021294 (ABALON)	5.2864	1	0	RS3seq-Chen2013+RS3target	-1.556	124			Aggregate CFD score > 4.80; Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2
+BCL2L1	5	9606	598	BCL2L1	NM_138578.3	BCL2L1	CRISPRko	CDS	NC_000020.11	-	SpyoCas9	5	1	1	+	antisense	31721969	CGCTTGCTTTACTGCTGCCA	TCAGCGCTTGCTTTACTGCTGCCATGGGGA	TGG	2	250	702	35.6	103021294 (ABALON)	0.8993	0	0	RS3seq-Chen2013+RS3target	-1.882	125			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 244
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150577454	CATGTAGAGGACCTAGAAGG	CTTCCATGTAGAGGACCTAGAAGGTGGCAT	TGG	3	974	1053	92.5		0.0000	0	0	RS3seq-Chen2013+RS3target	1.172	1			Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578249	GCTAGTTAAACAAAGAGGCT	ACTGGCTAGTTAAACAAAGAGGCTGGGTAA	GGG	2	931	1053	88.4		8.6289	0	0	RS3seq-Chen2013+RS3target	0.8847	2			Aggregate CFD score > 4.80; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578985	AGTCGCTGGAGATTATCTCT	CGGCAGTCGCTGGAGATTATCTCTCGGTAC	CGG	1	546	1053	51.9		0.0000	0	0	RS3seq-Chen2013+RS3target	0.7856	3	1	1	
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578892	AGGCGCTGGAGACCTTACGA	AGGAAGGCGCTGGAGACCTTACGACGGGTT	CGG	1	639	1053	60.7		0.0000	0	0	RS3seq-Chen2013+RS3target	0.7713	4	2	1	
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579143	GGAGCTGGACGGGTACGAGC	AAGAGGAGCTGGACGGGTACGAGCCGGAGC	CGG	1	388	1053	36.8		0.0000	0	0	RS3seq-Chen2013+RS3target	0.7469	5	3	1	
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579311	CCTCACGCCAGACTCCCGGA	CCACCCTCACGCCAGACTCCCGGAGGGTCG	GGG	1	220	1053	20.9		0.0000	0	0	RS3seq-Chen2013+RS3target	0.6846	6	4	1	
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579063	GTAATAACACCAGTACGGAC	TCTGGTAATAACACCAGTACGGACGGGTCA	GGG	1	468	1053	44.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.6757	7	5	1	
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578286	GTATCACAGACGTTCTCGTA	GAAAGTATCACAGACGTTCTCGTAAGGACA	AGG	2	894	1053	84.9		0.0000	0	0	RS3seq-Chen2013+RS3target	0.6455	8			Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578891	GGCGCTGGAGACCTTACGAC	GGAAGGCGCTGGAGACCTTACGACGGGTTG	GGG	1	640	1053	60.8		0.0000	0	0	RS3seq-Chen2013+RS3target	0.6300	9			Spacing Violation: Too close to earlier pick at position 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578912	GTCTGGGGCCACCAGCAGGA	GCAGGTCTGGGGCCACCAGCAGGAAGGCGC	AGG	1	619	1053	58.8		1.9285	0	0	RS3seq-Chen2013+RS3target	0.5828	10			Spacing Violation: Too close to earlier pick at position 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579009	AGGAGGAGGACGAGTTGTAC	GAGGAGGAGGAGGACGAGTTGTACCGGCAG	CGG	1	522	1053	49.6		0.0000	0	0	RS3seq-Chen2013+RS3target	0.5658	11			Spacing Violation: Too close to earlier pick at position 546
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150577403	GGTGTTGCTGGAGTAGGAGC	TGCAGGTGTTGCTGGAGTAGGAGCTGGTTT	TGG	3	1025	1053	97.3		1.3000	0	0	RS3seq-Chen2013+RS3target	0.4660	12			Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578848	AACCACGAGACGGCCTTCCA	GCGCAACCACGAGACGGCCTTCCAAGGTAA	AGG	1	683	1053	64.9		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4541	13			BsmBI:GAGACG; Spacing Violation: Too close to earlier pick at position 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578254	GACTGGCTAGTTAAACAAAG	ACGGGACTGGCTAGTTAAACAAAGAGGCTG	AGG	2	926	1053	87.9		0.9333	0	0	RS3seq-Chen2013+RS3target	0.4457	14			Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579132	GGTACGAGCCGGAGCCTCTC	GACGGGTACGAGCCGGAGCCTCTCGGGAAG	GGG	1	399	1053	37.9		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4442	15			Spacing Violation: Too close to earlier pick at position 388
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578858	CGTGCAGCGCAACCACGAGA	ATGGCGTGCAGCGCAACCACGAGACGGCCT	CGG	1	673	1053	63.9		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4413	16			Spacing Violation: Too close to earlier pick at position 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150577466	CACCTTCTAGGTCCTCTACA	ATGCCACCTTCTAGGTCCTCTACATGGAAG	TGG	3	962	1053	91.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4385	17			Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150578891	GCCATCCCCAACCCGTCGTA	GCACGCCATCCCCAACCCGTCGTAAGGTCT	AGG	1	640	1053	60.8		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4355	18			Spacing Violation: Too close to earlier pick at position 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579288	GGGACCTCGGCGCCAATGGG	GTCGGGGACCTCGGCGCCAATGGGCGGCGG	CGG	1	243	1053	23.1		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4168	19			Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579484	GGACTCAACCTCTACTGTGG	AATCGGACTCAACCTCTACTGTGGGGGGGC	GGG	1	47	1053	4.5		0.0000	0	0	RS3seq-Chen2013+RS3target	0.4024	20			Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579485	CGGACTCAACCTCTACTGTG	TAATCGGACTCAACCTCTACTGTGGGGGGG	GGG	1	46	1053	4.4		1.5264	0	0	RS3seq-Chen2013+RS3target	0.3992	21			Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579251	GAAGAAAAGCAGCCTCGCGG	GCGCGAAGAAAAGCAGCCTCGCGGGGGTCG	GGG	1	280	1053	26.6		0.0000	0	0	RS3seq-Chen2013+RS3target	0.3683	22			Quota Met
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578402	CAGCGACGGCGTAACAAACT	TTTTCAGCGACGGCGTAACAAACTGGGGCA	GGG	2	778	1053	73.9		0.0000	0	0	RS3seq-Chen2013+RS3target	0.3624	23			Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578962	TACCTTCGGGAGCAGGCCAC	TCGGTACCTTCGGGAGCAGGCCACCGGCGC	CGG	1	569	1053	54.0		1.3083	0	0	RS3seq-Chen2013+RS3target	0.3293	24			Spacing Violation: Too close to earlier pick at position 546
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579486	TCGGACTCAACCTCTACTGT	GTAATCGGACTCAACCTCTACTGTGGGGGG	GGG	1	45	1053	4.3		0.5000	0	0	RS3seq-Chen2013+RS3target	0.3287	25			Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150577457	TTCCATGTAGAGGACCTAGA	GTTCTTCCATGTAGAGGACCTAGAAGGTGG	AGG	3	971	1053	92.2		0.0000	0	0	RS3seq-Chen2013+RS3target	0.3063	26			Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150577447	AGGACCTAGAAGGTGGCATC	GTAGAGGACCTAGAAGGTGGCATCAGGAAT	AGG	3	981	1053	93.2		1.0092	0	0	RS3seq-Chen2013+RS3target	0.2966	27			Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150578846	ATGAACCCCCTTACCTTGGA	ATTAATGAACCCCCTTACCTTGGAAGGCCG	AGG	1	685	1053	65.1		0.0390	0	0	RS3seq-Chen2013+RS3target	0.2852	28			Outside Target Window: 5-65%; Spacing Violation: Too close to earlier pick at position 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579388	TCGGCCCGGCGAGAGATAGG	GGCCTCGGCCCGGCGAGAGATAGGGGGAGG	GGG	1	143	1053	13.6		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2770	29			Quota Met
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150578422	TTACGCCGTCGCTGAAAACA	TTTGTTACGCCGTCGCTGAAAACATGGATC	TGG	2	758	1053	72.0		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2707	30			Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579487	ATCGGACTCAACCTCTACTG	GGTAATCGGACTCAACCTCTACTGTGGGGG	TGG	1	44	1053	4.2		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2439	31			Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578276	CGTTCTCGTAAGGACAAAAC	CAGACGTTCTCGTAAGGACAAAACGGGACT	GGG	2	904	1053	85.8		0.8125	0	0	RS3seq-Chen2013+RS3target	0.2400	32			Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579001	AGATAATCTCCAGCGACTGC	CGAGAGATAATCTCCAGCGACTGCCGGTAC	CGG	1	530	1053	50.3		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2371	33			Spacing Violation: Too close to earlier pick at position 546
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579435	GTAGCCAAAAGTCGCCCTCC	CTCCGTAGCCAAAAGTCGCCCTCCCGGGCG	CGG	1	96	1053	9.1		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2339	34			Quota Met
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578843	CGAGACGGCCTTCCAAGGTA	ACCACGAGACGGCCTTCCAAGGTAAGGGGG	AGG	1	688	1053	65.3		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2327	35			BsmBI:GAGACG; Outside Target Window: 5-65%; Spacing Violation: Too close to earlier pick at position 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578401	AGCGACGGCGTAACAAACTG	TTTCAGCGACGGCGTAACAAACTGGGGCAG	GGG	2	779	1053	74.0		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2206	36			Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579068	ATCTGGTAATAACACCAGTA	GGGAATCTGGTAATAACACCAGTACGGACG	CGG	1	463	1053	44.0		0.7000	0	0	RS3seq-Chen2013+RS3target	0.2170	37			Spacing Violation: Too close to earlier pick at position 468
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150577467	TGTGGAGTTCTTCCATGTAG	GGTTTGTGGAGTTCTTCCATGTAGAGGACC	AGG	3	961	1053	91.3		0.0000	0	0	RS3seq-Chen2013+RS3target	0.2166	38			Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579323	CCTCCGGGAGTCTGGCGTGA	CGACCCTCCGGGAGTCTGGCGTGAGGGTGG	GGG	1	208	1053	19.8		0.3333	0	0	RS3seq-Chen2013+RS3target	0.2102	39			Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579512	GTTTGGCCTCAAAAGAAACG	CAATGTTTGGCCTCAAAAGAAACGCGGTAA	CGG	1	19	1053	1.8		0.8125	0	0	RS3seq-Chen2013+RS3target	0.2014	40			Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579129	ACAGCCGGCCGCTTCCCGAG	CAGGACAGCCGGCCGCTTCCCGAGAGGCTC	AGG	1	402	1053	38.2		0.2069	0	0	RS3seq-Chen2013+RS3target	0.1944	41			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 388
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578271	TCGTAAGGACAAAACGGGAC	GTTCTCGTAAGGACAAAACGGGACTGGCTA	TGG	2	909	1053	86.3		1.0000	1	0	RS3seq-Chen2013+RS3target	0.1742	42			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579158	CATGTCGCCCGAAGAGGAGC	CCATCATGTCGCCCGAAGAGGAGCTGGACG	TGG	1	373	1053	35.4		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1678	43			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 388
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579102	TCCCCGACCAACTCCAGCAG	AGATTCCCCGACCAACTCCAGCAGCGGCAG	CGG	1	429	1053	40.7		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1585	44			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 388, 468
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579161	GTACCCGTCCAGCTCCTCTT	GCTCGTACCCGTCCAGCTCCTCTTCGGGCG	CGG	1	370	1053	35.1		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1501	45			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 388
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579154	TCGCCCGAAGAGGAGCTGGA	CATGTCGCCCGAAGAGGAGCTGGACGGGTA	CGG	1	377	1053	35.8		1.4286	0	0	RS3seq-Chen2013+RS3target	0.1459	46			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 388
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150578857	TACCTTGGAAGGCCGTCTCG	CCCTTACCTTGGAAGGCCGTCTCGTGGTTG	TGG	1	674	1053	64.0		0.0000	0	0	RS3seq-Chen2013+RS3target	0.1403	47			BsmBI:CGTCTC; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578975	GATTATCTCTCGGTACCTTC	TGGAGATTATCTCTCGGTACCTTCGGGAGC	GGG	1	556	1053	52.8		0.3077	0	0	RS3seq-Chen2013+RS3target	0.1235	48			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 546
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579315	CCACCCTCACGCCAGACTCC	CCGTCCACCCTCACGCCAGACTCCCGGAGG	CGG	1	216	1053	20.5		1.1429	0	0	RS3seq-Chen2013+RS3target	0.09920	49			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579361	GAGGCCGGCGCGGTGATTGG	AGGGGAGGCCGGCGCGGTGATTGGCGGAAG	CGG	1	170	1053	16.1		0.6071	0	0	RS3seq-Chen2013+RS3target	0.09300	50			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578403	TCAGCGACGGCGTAACAAAC	GTTTTCAGCGACGGCGTAACAAACTGGGGC	TGG	2	777	1053	73.8		0.0000	0	0	RS3seq-Chen2013+RS3target	0.07961	51			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579194	GCTTGAGGAGATGGAAGCCC	CGCCGCTTGAGGAGATGGAAGCCCCGGCCG	CGG	1	337	1053	32.0		3.6019	2	0	RS3seq-Chen2013+RS3target	0.07814	52			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 388
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579334	CTGGCGTGAGGGTGGACGGG	GAGTCTGGCGTGAGGGTGGACGGGGGGCTT	GGG	1	197	1053	18.7		0.5714	0	0	RS3seq-Chen2013+RS3target	0.05218	53			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150577454	CATTCCTGATGCCACCTTCT	AGCACATTCCTGATGCCACCTTCTAGGTCC	AGG	3	974	1053	92.5		1.2758	0	0	RS3seq-Chen2013+RS3target	0.04143	54			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579482	ACTCAACCTCTACTGTGGGG	TCGGACTCAACCTCTACTGTGGGGGGGCCG	GGG	1	49	1053	4.7		0.0000	0	0	RS3seq-Chen2013+RS3target	0.03909	55			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579487	AGCCGGCCCCCCCACAGTAG	CCCAAGCCGGCCCCCCCACAGTAGAGGTTG	AGG	1	44	1053	4.2		0.0000	0	0	RS3seq-Chen2013+RS3target	0.02142	56			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578934	AGGACACAAAGCCAATGGGC	GCCAAGGACACAAAGCCAATGGGCAGGTCT	AGG	1	597	1053	56.7		3.0571	0	0	RS3seq-Chen2013+RS3target	0.01298	57			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 546, 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579133	GGGTACGAGCCGGAGCCTCT	GGACGGGTACGAGCCGGAGCCTCTCGGGAA	CGG	1	398	1053	37.8		0.0000	0	0	RS3seq-Chen2013+RS3target	0.01237	58			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 388
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579312	CCCTCACGCCAGACTCCCGG	TCCACCCTCACGCCAGACTCCCGGAGGGTC	AGG	1	219	1053	20.8		0.4545	0	0	RS3seq-Chen2013+RS3target	0.008731	59			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150578329	ATGGTTCGATGCAGCTTTCT	GCTAATGGTTCGATGCAGCTTTCTTGGTTT	TGG	2	851	1053	80.8		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.01633	60			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150578971	CGCCGGTGGCCTGCTCCCGA	TTGGCGCCGGTGGCCTGCTCCCGAAGGTAC	AGG	1	560	1053	53.2		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.01813	61			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 546
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579390	CCTCGGCCCGGCGAGAGATA	GAGGCCTCGGCCCGGCGAGAGATAGGGGGA	GGG	1	141	1053	13.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.03563	62			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579252	ACGTCACCGCGACCCCCGCG	CCCGACGTCACCGCGACCCCCGCGAGGCTG	AGG	1	279	1053	26.5		1.3613	0	0	RS3seq-Chen2013+RS3target	-0.04422	63			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579333	TCTGGCGTGAGGGTGGACGG	GGAGTCTGGCGTGAGGGTGGACGGGGGGCT	GGG	1	198	1053	18.8		0.5000	0	0	RS3seq-Chen2013+RS3target	-0.04452	64			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579162	TACCCGTCCAGCTCCTCTTC	CTCGTACCCGTCCAGCTCCTCTTCGGGCGA	GGG	1	369	1053	35.0		1.3636	0	0	RS3seq-Chen2013+RS3target	-0.04518	65			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 388
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150578934	GTGGCCCCAGACCTGCCCAT	GCTGGTGGCCCCAGACCTGCCCATTGGCTT	TGG	1	597	1053	56.7		2.8588	0	0	RS3seq-Chen2013+RS3target	-0.04722	66			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 546, 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578250	GGCTAGTTAAACAAAGAGGC	GACTGGCTAGTTAAACAAAGAGGCTGGGTA	TGG	2	930	1053	88.3		11.0845	0	0	RS3seq-Chen2013+RS3target	-0.06858	67			Aggregate CFD score > 4.80; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579065	GGGTAGTGACCCGTCCGTAC	TCGAGGGTAGTGACCCGTCCGTACTGGTGT	TGG	1	466	1053	44.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.07355	68			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 468
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579249	GCGAAGAAAAGCAGCCTCGC	GGGCGCGAAGAAAAGCAGCCTCGCGGGGGT	GGG	1	282	1053	26.8		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.08776	69			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150578912	GGTCTCCAGCGCCTTCCTGC	GTAAGGTCTCCAGCGCCTTCCTGCTGGTGG	TGG	1	619	1053	58.8		2.9102	0	0	RS3seq-Chen2013+RS3target	-0.08969	70			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579368	GCTTCCGCCAATCACCGCGC	CGGCGCTTCCGCCAATCACCGCGCCGGCCT	CGG	1	163	1053	15.5		0.0222	0	0	RS3seq-Chen2013+RS3target	-0.1106	71			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579289	GTCGCGCGGCCGCCGCCCAT	GAGGGTCGCGCGGCCGCCGCCCATTGGCGC	TGG	1	242	1053	23.0		1.4966	0	0	RS3seq-Chen2013+RS3target	-0.1115	72			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579352	GCGGTGATTGGCGGAAGCGC	CGGCGCGGTGATTGGCGGAAGCGCCGGCGC	CGG	1	179	1053	17.0		0.7143	0	0	RS3seq-Chen2013+RS3target	-0.1215	73			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579438	GCGGCGCCACCCGCCCGGGA	AGCGGCGGCGCCACCCGCCCGGGAGGGCGA	GGG	1	93	1053	8.8		0.6818	0	0	RS3seq-Chen2013+RS3target	-0.1299	74			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579285	TCGGGGACCTCGGCGCCAAT	GACGTCGGGGACCTCGGCGCCAATGGGCGG	GGG	1	246	1053	23.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1308	75			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579322	CCCTCCGGGAGTCTGGCGTG	GCGACCCTCCGGGAGTCTGGCGTGAGGGTG	AGG	1	209	1053	19.8		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1523	76			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578938	GCCAAGGACACAAAGCCAAT	CGGCGCCAAGGACACAAAGCCAATGGGCAG	GGG	1	593	1053	56.3		1.2167	0	0	RS3seq-Chen2013+RS3target	-0.1621	77			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 546, 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579436	TAGCCAAAAGTCGCCCTCCC	TCCGTAGCCAAAAGTCGCCCTCCCGGGCGG	GGG	1	95	1053	9.0		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1649	78			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579344	GGTGGACGGGGGGCTTGCGC	TGAGGGTGGACGGGGGGCTTGCGCCGGCGC	CGG	1	187	1053	17.8		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1698	79			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150577398	TGCTGGAGTAGGAGCTGGTT	GTGTTGCTGGAGTAGGAGCTGGTTTGGCAT	TGG	3	1030	1053	97.8		0.6527	0	0	RS3seq-Chen2013+RS3target	-0.1907	80			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578887	CTGGAGACCTTACGACGGGT	GGCGCTGGAGACCTTACGACGGGTTGGGGA	TGG	1	644	1053	61.2		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.1949	81			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579029	GACGCCGCCGCCAGCAGAGG	CCTCGACGCCGCCGCCAGCAGAGGAGGAGG	AGG	1	502	1053	47.7		2.4690	1	0	RS3seq-Chen2013+RS3target	-0.2202	82			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 468, 546
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578886	TGGAGACCTTACGACGGGTT	GCGCTGGAGACCTTACGACGGGTTGGGGAT	GGG	1	645	1053	61.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2203	83			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579291	ACCTCGGCGCCAATGGGCGG	GGGGACCTCGGCGCCAATGGGCGGCGGCCG	CGG	1	240	1053	22.8		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2215	84			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579064	GGTAATAACACCAGTACGGA	ATCTGGTAATAACACCAGTACGGACGGGTC	CGG	1	467	1053	44.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.2372	85			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 468
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578906	GGCCACCAGCAGGAAGGCGC	CTGGGGCCACCAGCAGGAAGGCGCTGGAGA	TGG	1	625	1053	59.4		0.7837	0	0	RS3seq-Chen2013+RS3target	-0.2411	86			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578976	AGATTATCTCTCGGTACCTT	CTGGAGATTATCTCTCGGTACCTTCGGGAG	CGG	1	555	1053	52.7		1.2231	0	0	RS3seq-Chen2013+RS3target	-0.2470	87			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 546
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579122	GGAGCCTCTCGGGAAGCGGC	AGCCGGAGCCTCTCGGGAAGCGGCCGGCTG	CGG	1	409	1053	38.8		0.6875	0	0	RS3seq-Chen2013+RS3target	-0.3028	88			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 388
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579032	CTCGACGCCGCCGCCAGCAG	TACCCTCGACGCCGCCGCCAGCAGAGGAGG	AGG	1	499	1053	47.4		0.5000	0	0	RS3seq-Chen2013+RS3target	-0.3100	89			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 468, 546
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579085	CTGGAGTTGGTCGGGGAATC	GCTGCTGGAGTTGGTCGGGGAATCTGGTAA	TGG	1	446	1053	42.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3112	90			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 468
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579114	TCCAGCAGCGGCAGGACAGC	CAACTCCAGCAGCGGCAGGACAGCCGGCCG	CGG	1	417	1053	39.6		2.8578	0	0	RS3seq-Chen2013+RS3target	-0.3130	91			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 388, 468
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578277	ACGTTCTCGTAAGGACAAAA	ACAGACGTTCTCGTAAGGACAAAACGGGAC	CGG	2	903	1053	85.8		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3244	92			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579030	TCGTCCTCCTCCTCCTCTGC	CAACTCGTCCTCCTCCTCCTCTGCTGGCGG	TGG	1	501	1053	47.6		30.2695	6	3	RS3seq-Chen2013+RS3target	-0.3264	93			Aggregate CFD score > 4.80; Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 468, 546
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578927	AAAGCCAATGGGCAGGTCTG	ACACAAAGCCAATGGGCAGGTCTGGGGCCA	GGG	1	604	1053	57.4		0.6190	0	0	RS3seq-Chen2013+RS3target	-0.3453	94			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579023	GCCGCCAGCAGAGGAGGAGG	CGCCGCCGCCAGCAGAGGAGGAGGAGGACG	AGG	1	508	1053	48.2		14.1605	1	0	RS3seq-Chen2013+RS3target	-0.3503	95			Aggregate CFD score > 4.80; Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 468, 546
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579045	TCTGCTGGCGGCGGCGTCGA	CTCCTCTGCTGGCGGCGGCGTCGAGGGTAG	GGG	1	486	1053	46.2		0.7176	0	0	RS3seq-Chen2013+RS3target	-0.3508	96			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 468
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150577409	TTTGCAGGTGTTGCTGGAGT	GGCTTTTGCAGGTGTTGCTGGAGTAGGAGC	AGG	3	1019	1053	96.8		0.1765	0	0	RS3seq-Chen2013+RS3target	-0.3563	97			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579220	CCTCAAGCGGCGCCGCGCGG	ATCTCCTCAAGCGGCGCCGCGCGGCGGGTG	CGG	1	311	1053	29.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.3576	98			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579026	GCCGCCGCCAGCAGAGGAGG	CGACGCCGCCGCCAGCAGAGGAGGAGGAGG	AGG	1	505	1053	48.0		3.3471	0	0	RS3seq-Chen2013+RS3target	-0.3781	99			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 468, 546
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579207	GGGGCTTCCATCTCCTCAAG	GGCCGGGGCTTCCATCTCCTCAAGCGGCGC	CGG	1	324	1053	30.8		0.3636	0	0	RS3seq-Chen2013+RS3target	-0.3991	100			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579248	CGCGAAGAAAAGCAGCCTCG	TGGGCGCGAAGAAAAGCAGCCTCGCGGGGG	CGG	1	283	1053	26.9		1.5901	0	0	RS3seq-Chen2013+RS3target	-0.3995	101			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579303	CAGACTCCCGGAGGGTCGCG	ACGCCAGACTCCCGGAGGGTCGCGCGGCCG	CGG	1	228	1053	21.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4184	102			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579284	GTCGGGGACCTCGGCGCCAA	TGACGTCGGGGACCTCGGCGCCAATGGGCG	TGG	1	247	1053	23.5		0.3000	0	0	RS3seq-Chen2013+RS3target	-0.4318	103			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579529	CGGCGGCGACTGGCAATGTT	GCGGCGGCGGCGACTGGCAATGTTTGGCCT	TGG	1	2	1053	0.2		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4336	104			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578916	GCAGGTCTGGGGCCACCAGC	ATGGGCAGGTCTGGGGCCACCAGCAGGAAG	AGG	1	615	1053	58.4		3.8805	0	0	RS3seq-Chen2013+RS3target	-0.4423	105			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579332	GTCTGGCGTGAGGGTGGACG	GGGAGTCTGGCGTGAGGGTGGACGGGGGGC	GGG	1	199	1053	18.9		0.0500	0	0	RS3seq-Chen2013+RS3target	-0.4466	106			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579439	CCAAAAGTCGCCCTCCCGGG	GTAGCCAAAAGTCGCCCTCCCGGGCGGGTG	CGG	1	92	1053	8.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4485	107			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579391	GCCTCGGCCCGGCGAGAGAT	GGAGGCCTCGGCCCGGCGAGAGATAGGGGG	AGG	1	140	1053	13.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4547	108			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579094	CTGCCGCTGCTGGAGTTGGT	TGTCCTGCCGCTGCTGGAGTTGGTCGGGGA	CGG	1	437	1053	41.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.4629	109			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 388, 468
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579470	CTGTGGGGGGGCCGGCTTGG	TCTACTGTGGGGGGGCCGGCTTGGGGGCCG	GGG	1	61	1053	5.8		0.4000	0	0	RS3seq-Chen2013+RS3target	-0.4678	110			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578881	ACCTTACGACGGGTTGGGGA	GGAGACCTTACGACGGGTTGGGGATGGCGT	TGG	1	650	1053	61.7		0.7143	0	0	RS3seq-Chen2013+RS3target	-0.4772	111			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150577434	TGGCATCAGGAATGTGCTGC	AAGGTGGCATCAGGAATGTGCTGCTGGCTT	TGG	3	994	1053	94.4		1.0359	0	0	RS3seq-Chen2013+RS3target	-0.4798	112			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579126	AGCCGGAGCCTCTCGGGAAG	TACGAGCCGGAGCCTCTCGGGAAGCGGCCG	CGG	1	405	1053	38.5		0.7059	0	0	RS3seq-Chen2013+RS3target	-0.4857	113			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 388
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579250	CGAAGAAAAGCAGCCTCGCG	GGCGCGAAGAAAAGCAGCCTCGCGGGGGTC	GGG	1	281	1053	26.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.5027	114			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150578954	TGGCTTTGTGTCCTTGGCGC	CCATTGGCTTTGTGTCCTTGGCGCCGGTGG	CGG	1	577	1053	54.8		0.4444	0	0	RS3seq-Chen2013+RS3target	-0.5116	115			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 546
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579331	AGTCTGGCGTGAGGGTGGAC	CGGGAGTCTGGCGTGAGGGTGGACGGGGGG	GGG	1	200	1053	19.0		2.1512	0	0	RS3seq-Chen2013+RS3target	-0.5184	116			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578397	ACGGCGTAACAAACTGGGGC	AGCGACGGCGTAACAAACTGGGGCAGGATT	AGG	2	783	1053	74.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.5196	117			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579380	GCGAGAGATAGGGGGAGGGG	CCCGGCGAGAGATAGGGGGAGGGGAGGCCG	AGG	1	151	1053	14.3		9.5727	1	0	RS3seq-Chen2013+RS3target	-0.5197	118			Aggregate CFD score > 4.80; Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579092	GCCGCTGCTGGAGTTGGTCG	TCCTGCCGCTGCTGGAGTTGGTCGGGGAAT	GGG	1	439	1053	41.7		0.1250	0	0	RS3seq-Chen2013+RS3target	-0.5222	119			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 388, 468
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578416	ATGATCCATGTTTTCAGCGA	AGTGATGATCCATGTTTTCAGCGACGGCGT	CGG	2	764	1053	72.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.5395	120			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; poly(T):TTTT
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579389	CTCGGCCCGGCGAGAGATAG	AGGCCTCGGCCCGGCGAGAGATAGGGGGAG	GGG	1	142	1053	13.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.5505	121			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579135	GGCCGCTTCCCGAGAGGCTC	AGCCGGCCGCTTCCCGAGAGGCTCCGGCTC	CGG	1	396	1053	37.6		0.3000	0	0	RS3seq-Chen2013+RS3target	-0.5559	122			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 388
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579308	CGGCGGCCGCGCGACCCTCC	TGGGCGGCGGCCGCGCGACCCTCCGGGAGT	GGG	1	223	1053	21.2		0.2143	0	0	RS3seq-Chen2013+RS3target	-0.5704	123			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579402	CGGAGAAGGAGGCCTCGGCC	GCTACGGAGAAGGAGGCCTCGGCCCGGCGA	CGG	1	129	1053	12.3		1.0370	0	0	RS3seq-Chen2013+RS3target	-0.5707	124			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579364	GGGGAGGCCGGCGCGGTGAT	GGGAGGGGAGGCCGGCGCGGTGATTGGCGG	TGG	1	167	1053	15.9		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.5713	125			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579182	GGGCGACATGATGGCGTCAG	CTTCGGGCGACATGATGGCGTCAGCGGCCG	CGG	1	349	1053	33.1		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.5776	126			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 388
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579505	CTCAAAAGAAACGCGGTAAT	TGGCCTCAAAAGAAACGCGGTAATCGGACT	CGG	1	26	1053	2.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.5784	127			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579225	AGCGGCGCCGCGCGGCGGGT	CTCAAGCGGCGCCGCGCGGCGGGTGGGCGC	GGG	1	306	1053	29.1		3.3003	0	0	RS3seq-Chen2013+RS3target	-0.5875	128			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579483	GACTCAACCTCTACTGTGGG	ATCGGACTCAACCTCTACTGTGGGGGGGCC	GGG	1	48	1053	4.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.6322	129			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579268	CGGGGGTCGCGGTGACGTCG	CTCGCGGGGGTCGCGGTGACGTCGGGGACC	GGG	1	263	1053	25.0		0.5882	0	0	RS3seq-Chen2013+RS3target	-0.6357	130			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150578336	GATGCAGCTTTCTTGGTTTA	GTTCGATGCAGCTTTCTTGGTTTATGGTCT	TGG	2	844	1053	80.2		0.5333	0	0	RS3seq-Chen2013+RS3target	-0.6505	131			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579307	GCGGCGGCCGCGCGACCCTC	ATGGGCGGCGGCCGCGCGACCCTCCGGGAG	CGG	1	224	1053	21.3		0.8500	0	0	RS3seq-Chen2013+RS3target	-0.6561	132			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579033	TCCTCCTCCTCCTCTGCTGG	CTCGTCCTCCTCCTCCTCTGCTGGCGGCGG	CGG	1	498	1053	47.3		6.9914	1	0	RS3seq-Chen2013+RS3target	-0.6691	133			Aggregate CFD score > 4.80; Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 468, 546
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579266	CGCGGGGGTCGCGGTGACGT	GCCTCGCGGGGGTCGCGGTGACGTCGGGGA	CGG	1	265	1053	25.2		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.6708	134			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579471	ACTGTGGGGGGGCCGGCTTG	CTCTACTGTGGGGGGGCCGGCTTGGGGGCC	GGG	1	60	1053	5.7		0.3846	0	0	RS3seq-Chen2013+RS3target	-0.6819	135			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579186	GACATGATGGCGTCAGCGGC	GGGCGACATGATGGCGTCAGCGGCCGGGGC	CGG	1	345	1053	32.8		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.6887	136			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 388
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150578360	CTTCAAGTGTTTAGCCACAA	TGGTCTTCAAGTGTTTAGCCACAAAGGCAC	AGG	2	820	1053	77.9		0.1333	0	0	RS3seq-Chen2013+RS3target	-0.6893	137			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579217	TCTCCTCAAGCGGCGCCGCG	TCCATCTCCTCAAGCGGCGCCGCGCGGCGG	CGG	1	314	1053	29.8		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.6923	138			BsmBI:5'cgTCTC; On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579470	GCCGCTGCCGGCCCCCAAGC	CGCCGCCGCTGCCGGCCCCCAAGCCGGCCC	CGG	1	61	1053	5.8		0.9846	0	0	RS3seq-Chen2013+RS3target	-0.6932	139			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578885	GGAGACCTTACGACGGGTTG	CGCTGGAGACCTTACGACGGGTTGGGGATG	GGG	1	646	1053	61.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.7025	140			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579275	CGCGGTGACGTCGGGGACCT	GGGTCGCGGTGACGTCGGGGACCTCGGCGC	CGG	1	256	1053	24.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.7166	141			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579224	AAGCGGCGCCGCGCGGCGGG	CCTCAAGCGGCGCCGCGCGGCGGGTGGGCG	TGG	1	307	1053	29.2		5.6975	0	0	RS3seq-Chen2013+RS3target	-0.7284	142			Aggregate CFD score > 4.80; On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579326	CCGGGAGTCTGGCGTGAGGG	CCCTCCGGGAGTCTGGCGTGAGGGTGGACG	TGG	1	205	1053	19.5		2.0452	0	0	RS3seq-Chen2013+RS3target	-0.7287	143			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579221	CTTCGCGCCCACCCGCCGCG	TTTTCTTCGCGCCCACCCGCCGCGCGGCGC	CGG	1	310	1053	29.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.7303	144			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579106	CGACCAACTCCAGCAGCGGC	TCCCCGACCAACTCCAGCAGCGGCAGGACA	AGG	1	425	1053	40.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.7326	145			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 388, 468
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579395	CCCTCCCCCTATCTCTCGCC	CCTCCCCTCCCCCTATCTCTCGCCGGGCCG	GGG	1	136	1053	12.9		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.7345	146			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579267	GCGGGGGTCGCGGTGACGTC	CCTCGCGGGGGTCGCGGTGACGTCGGGGAC	GGG	1	264	1053	25.1		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.7384	147			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579173	CTCCTCTTCGGGCGACATGA	CCAGCTCCTCTTCGGGCGACATGATGGCGT	TGG	1	358	1053	34.0		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.7601	148			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 388
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579153	CGCCCGAAGAGGAGCTGGAC	ATGTCGCCCGAAGAGGAGCTGGACGGGTAC	GGG	1	378	1053	35.9		0.3462	0	0	RS3seq-Chen2013+RS3target	-0.7723	149			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 388
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579413	ACTTTTGGCTACGGAGAAGG	GGCGACTTTTGGCTACGGAGAAGGAGGCCT	AGG	1	118	1053	11.2		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.7956	150			On-Target Efficacy Score < 0.2; poly(T):TTTT
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579457	GGCTTGGGGGCCGGCAGCGG	GGCCGGCTTGGGGGCCGGCAGCGGCGGCGC	CGG	1	74	1053	7.0		1.6341	0	0	RS3seq-Chen2013+RS3target	-0.7968	151			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578939	CGCCAAGGACACAAAGCCAA	CCGGCGCCAAGGACACAAAGCCAATGGGCA	TGG	1	592	1053	56.2		3.2429	2	0	RS3seq-Chen2013+RS3target	-0.8041	152			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 546, 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579221	CTCAAGCGGCGCCGCGCGGC	TCTCCTCAAGCGGCGCCGCGCGGCGGGTGG	GGG	1	310	1053	29.4		0.7788	0	0	RS3seq-Chen2013+RS3target	-0.8078	153			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150578310	TCTGTGATACTTTCTGCTAA	AACGTCTGTGATACTTTCTGCTAATGGTTC	TGG	2	870	1053	82.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.8171	154			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578999	CGAGTTGTACCGGCAGTCGC	AGGACGAGTTGTACCGGCAGTCGCTGGAGA	TGG	1	532	1053	50.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.8320	155			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 546
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578929	ACAAAGCCAATGGGCAGGTC	GGACACAAAGCCAATGGGCAGGTCTGGGGC	TGG	1	602	1053	57.2		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.8372	156			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579330	GAGTCTGGCGTGAGGGTGGA	CCGGGAGTCTGGCGTGAGGGTGGACGGGGG	CGG	1	201	1053	19.1		1.2381	0	0	RS3seq-Chen2013+RS3target	-0.8381	157			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579440	CAAAAGTCGCCCTCCCGGGC	TAGCCAAAAGTCGCCCTCCCGGGCGGGTGG	GGG	1	91	1053	8.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.8505	158			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578480	CTCAGGCATGCTTCGGAAAC	CTTTCTCAGGCATGCTTCGGAAACTGGACA	TGG	2	700	1053	66.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.8531	159			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579315	CGCGCGACCCTCCGGGAGTC	CGGCCGCGCGACCCTCCGGGAGTCTGGCGT	TGG	1	216	1053	20.5		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.8609	160			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579098	TGTCCTGCCGCTGCTGGAGT	CGGCTGTCCTGCCGCTGCTGGAGTTGGTCG	TGG	1	433	1053	41.1		2.3238	0	0	RS3seq-Chen2013+RS3target	-0.8808	161			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 388, 468
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579203	CGCGGCGCCGCTTGAGGAGA	GCCGCGCGGCGCCGCTTGAGGAGATGGAAG	TGG	1	328	1053	31.1		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.8816	162			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579394	CCCCTCCCCCTATCTCTCGC	GCCTCCCCTCCCCCTATCTCTCGCCGGGCC	CGG	1	137	1053	13.0		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.8874	163			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150577424	AATGTGCTGCTGGCTTTTGC	CAGGAATGTGCTGCTGGCTTTTGCAGGTGT	AGG	3	1004	1053	95.3		2.7939	0	0	RS3seq-Chen2013+RS3target	-0.8979	164			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; poly(T):TTTT
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579384	CCCGGCGAGAGATAGGGGGA	TCGGCCCGGCGAGAGATAGGGGGAGGGGAG	GGG	1	147	1053	14.0		1.0503	0	0	RS3seq-Chen2013+RS3target	-0.9190	165			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579442	AGCGGCGGCGCCACCCGCCC	CGGCAGCGGCGGCGCCACCCGCCCGGGAGG	GGG	1	89	1053	8.5		2.1761	2	0	RS3seq-Chen2013+RS3target	-0.9318	166			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579371	AGGGGGAGGGGAGGCCGGCG	AGATAGGGGGAGGGGAGGCCGGCGCGGTGA	CGG	1	160	1053	15.2		13.9903	2	0	RS3seq-Chen2013+RS3target	-0.9394	167			Aggregate CFD score > 4.80; Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578374	ATTGTGACTCTCATTTCTTT	CAGGATTGTGACTCTCATTTCTTTTGGTGC	TGG	2	806	1053	76.5		1.1042	0	0	RS3seq-Chen2013+RS3target	-0.9456	168			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150578915	CTCCAGCGCCTTCCTGCTGG	AGGTCTCCAGCGCCTTCCTGCTGGTGGCCC	TGG	1	616	1053	58.5		1.4180	0	0	RS3seq-Chen2013+RS3target	-0.9458	169			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579376	GAGATAGGGGGAGGGGAGGC	GCGAGAGATAGGGGGAGGGGAGGCCGGCGC	CGG	1	155	1053	14.7		4.6735	0	0	RS3seq-Chen2013+RS3target	-0.9612	170			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579257	AAGCAGCCTCGCGGGGGTCG	AGAAAAGCAGCCTCGCGGGGGTCGCGGTGA	CGG	1	274	1053	26.0		0.4286	0	0	RS3seq-Chen2013+RS3target	-0.9690	171			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579443	CAGCGGCGGCGCCACCCGCC	CCGGCAGCGGCGGCGCCACCCGCCCGGGAG	CGG	1	88	1053	8.4		1.2593	1	0	RS3seq-Chen2013+RS3target	-0.9745	172			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578954	GGAGCAGGCCACCGGCGCCA	TTCGGGAGCAGGCCACCGGCGCCAAGGACA	AGG	1	577	1053	54.8		0.7143	0	0	RS3seq-Chen2013+RS3target	-0.9872	173			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 546
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578969	CTCTCGGTACCTTCGGGAGC	TTATCTCTCGGTACCTTCGGGAGCAGGCCA	AGG	1	562	1053	53.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.9912	174			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 546
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579385	GCCCGGCGAGAGATAGGGGG	CTCGGCCCGGCGAGAGATAGGGGGAGGGGA	AGG	1	146	1053	13.9		0.0000	0	0	RS3seq-Chen2013+RS3target	-0.9955	175			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579407	GGCTACGGAGAAGGAGGCCT	TTTTGGCTACGGAGAAGGAGGCCTCGGCCC	CGG	1	124	1053	11.8		0.5000	0	0	RS3seq-Chen2013+RS3target	-1.069	176			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578487	TTTCTTTCTCAGGCATGCTT	TGCTTTTCTTTCTCAGGCATGCTTCGGAAA	CGG	2	693	1053	65.8		0.7346	0	0	RS3seq-Chen2013+RS3target	-1.070	177			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579439	GGCGGCGCCACCCGCCCGGG	CAGCGGCGGCGCCACCCGCCCGGGAGGGCG	AGG	1	92	1053	8.7		0.7857	0	0	RS3seq-Chen2013+RS3target	-1.073	178			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579093	TGCCGCTGCTGGAGTTGGTC	GTCCTGCCGCTGCTGGAGTTGGTCGGGGAA	GGG	1	438	1053	41.6		0.1071	0	0	RS3seq-Chen2013+RS3target	-1.077	179			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 388, 468
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579416	GCGACTTTTGGCTACGGAGA	GAGGGCGACTTTTGGCTACGGAGAAGGAGG	AGG	1	115	1053	10.9		0.9333	0	0	RS3seq-Chen2013+RS3target	-1.090	180			On-Target Efficacy Score < 0.2; poly(T):TTTT
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579036	TCCTCCTCCTCTGCTGGCGG	GTCCTCCTCCTCCTCTGCTGGCGGCGGCGT	CGG	1	495	1053	47.0		0.4705	0	0	RS3seq-Chen2013+RS3target	-1.107	181			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 468, 546
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579164	CGCCATCATGTCGCCCGAAG	CTGACGCCATCATGTCGCCCGAAGAGGAGC	AGG	1	367	1053	34.9		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.128	182			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 388
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579187	ACATGATGGCGTCAGCGGCC	GGCGACATGATGGCGTCAGCGGCCGGGGCT	GGG	1	344	1053	32.7		1.8077	1	0	RS3seq-Chen2013+RS3target	-1.141	183			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 388
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150577485	TGTTTTCTAGGATGGGTTTG	TTTTTGTTTTCTAGGATGGGTTTGTGGAGT	TGG	3	943	1053	89.6		1.3333	1	0	RS3seq-Chen2013+RS3target	-1.151	184			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; poly(T):TTTT
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579209	CCGCCGCGCGGCGCCGCTTG	CCACCCGCCGCGCGGCGCCGCTTGAGGAGA	AGG	1	322	1053	30.6		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.168	185			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579104	GCCGGCTGTCCTGCCGCTGC	AGCGGCCGGCTGTCCTGCCGCTGCTGGAGT	TGG	1	427	1053	40.6		1.3333	0	0	RS3seq-Chen2013+RS3target	-1.172	186			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier picks at positions 388, 468
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579383	CCGGCGAGAGATAGGGGGAG	CGGCCCGGCGAGAGATAGGGGGAGGGGAGG	GGG	1	148	1053	14.1		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.200	187			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150578957	CTTTGTGTCCTTGGCGCCGG	TTGGCTTTGTGTCCTTGGCGCCGGTGGCCT	TGG	1	574	1053	54.5		0.0222	0	0	RS3seq-Chen2013+RS3target	-1.204	188			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 546
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579188	CATGATGGCGTCAGCGGCCG	GCGACATGATGGCGTCAGCGGCCGGGGCTT	GGG	1	343	1053	32.6		0.5000	0	0	RS3seq-Chen2013+RS3target	-1.209	189			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 388
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579422	GGGAGGGCGACTTTTGGCTA	GCCCGGGAGGGCGACTTTTGGCTACGGAGA	CGG	1	109	1053	10.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.234	190			On-Target Efficacy Score < 0.2; poly(T):TTTT
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579428	CCGCCCGGGAGGGCGACTTT	CCACCCGCCCGGGAGGGCGACTTTTGGCTA	TGG	1	103	1053	9.8		0.3333	0	0	RS3seq-Chen2013+RS3target	-1.235	191			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579044	CTCTGCTGGCGGCGGCGTCG	CCTCCTCTGCTGGCGGCGGCGTCGAGGGTA	AGG	1	487	1053	46.2		1.4286	0	0	RS3seq-Chen2013+RS3target	-1.253	192			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 468
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579443	AAGTCGCCCTCCCGGGCGGG	CCAAAAGTCGCCCTCCCGGGCGGGTGGCGC	TGG	1	88	1053	8.4		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.266	193			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579466	GGGGGGGCCGGCTTGGGGGC	CTGTGGGGGGGCCGGCTTGGGGGCCGGCAG	CGG	1	65	1053	6.2		4.1387	2	1	RS3seq-Chen2013+RS3target	-1.272	194			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578928	CAAAGCCAATGGGCAGGTCT	GACACAAAGCCAATGGGCAGGTCTGGGGCC	GGG	1	603	1053	57.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.275	195			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 639
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579472	TACTGTGGGGGGGCCGGCTT	CCTCTACTGTGGGGGGGCCGGCTTGGGGGC	GGG	1	59	1053	5.6		0.3846	0	0	RS3seq-Chen2013+RS3target	-1.316	196			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579478	AACCTCTACTGTGGGGGGGC	ACTCAACCTCTACTGTGGGGGGGCCGGCTT	CGG	1	53	1053	5.0		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.336	197			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150578363	CATTTCTTTTGGTGCCTTTG	CTCTCATTTCTTTTGGTGCCTTTGTGGCTA	TGG	2	817	1053	77.6		5.7801	3	0	RS3seq-Chen2013+RS3target	-1.388	198			Aggregate CFD score > 4.80; Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; poly(T):TTTT
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579458	GCGGGTGGCGCCGCCGCTGC	CCGGGCGGGTGGCGCCGCCGCTGCCGGCCC	CGG	1	73	1053	6.9		0.0612	0	0	RS3seq-Chen2013+RS3target	-1.414	199			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150578948	GCCCATTGGCTTTGTGTCCT	ACCTGCCCATTGGCTTTGTGTCCTTGGCGC	TGG	1	583	1053	55.4		2.2035	0	0	RS3seq-Chen2013+RS3target	-1.463	200			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 546
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579281	GCCGCCGCCCATTGGCGCCG	CGCGGCCGCCGCCCATTGGCGCCGAGGTCC	AGG	1	250	1053	23.7		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.472	201			On-Target Efficacy Score < 0.2; Spacing Violation: Too close to earlier pick at position 220
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579473	CTACTGTGGGGGGGCCGGCT	ACCTCTACTGTGGGGGGGCCGGCTTGGGGG	TGG	1	58	1053	5.5		0.9333	0	0	RS3seq-Chen2013+RS3target	-1.562	202			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150579460	GCCGGCTTGGGGGCCGGCAG	GGGGGCCGGCTTGGGGGCCGGCAGCGGCGG	CGG	1	71	1053	6.7		3.5626	0	0	RS3seq-Chen2013+RS3target	-1.576	203			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150577492	TCTTTTTTGTTTTCTAGGAT	CTTTTCTTTTTTGTTTTCTAGGATGGGTTT	GGG	3	936	1053	88.9		2.6251	1	0	RS3seq-Chen2013+RS3target	-1.730	204			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; poly(T):TTTTTT
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579401	CCCTATCTCTCGCCGGGCCG	CTCCCCCTATCTCTCGCCGGGCCGAGGCCT	AGG	1	130	1053	12.3		0.0000	0	0	RS3seq-Chen2013+RS3target	-1.839	205			On-Target Efficacy Score < 0.2
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	+	antisense	150579517	CGATTACCGCGTTTCTTTTG	AGTCCGATTACCGCGTTTCTTTTGAGGCCA	AGG	1	14	1053	1.3		1.7778	1	0	RS3seq-Chen2013+RS3target	-1.905	206			Off-target CFD100 matches > 0; On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; poly(T):TTTT
+MCL1	5	9606	4170	MCL1	NM_021960.5	MCL1	CRISPRko	CDS	NC_000001.11	-	SpyoCas9	5	1	1	-	sense	150577415	CTGGCTTTTGCAGGTGTTGC	GCTGCTGGCTTTTGCAGGTGTTGCTGGAGT	TGG	3	1013	1053	96.2		1.1823	0	0	RS3seq-Chen2013+RS3target	-1.953	207			On-Target Efficacy Score < 0.2; Outside Target Window: 5-65%; poly(T):TTTT