Fix typos

Author: susannasiebert

04-outputs.Rmd

@@ -89,7 +89,7 @@ enabled additional filtering on related metrics:
   `--percentile-threshold` set, this parameter will influence how both the
   binding cutoff and the percentile cutoff are applied. The default,
   `conservative`, will require a candidate to pass both the binding and the
-  percentile threshold while the `exploratory` option will require a candiate
+  percentile threshold while the `exploratory` option will require a candidate
   to only pass either the binding or the percentile threshold.
 
 ### Coverage Filter

05-pvacview_tour.Rmd

@@ -92,7 +92,7 @@ highlighted with a green border.
 
 Next, this table lists the IC50 peptide MHC binding affinity for both the Best Peptide
 (MT) and the matched wild type (WT). These values are either a median of all of the
-binding predictions made by the predictiors selected in your pVACseq run, or
+binding predictions made by the predictors selected in your pVACseq run, or
 the lowest binding prediction made. This depends on the value set for the
 `--top-score-metric` in your run. The table also shows the median/lowest percentile
 scores of all predictors that provide this value, again depending on the
@@ -279,7 +279,7 @@ ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCG
 ## Regenerate Tiers with Custom Parameters
 
 During review of your data it might become apparent that different tiering
-thresholds would've been more approriate. pVACview allows you to re-tier your
+thresholds would've been more appropriate. pVACview allows you to re-tier your
 data with custom parameters by adjusting the sliders and inputs in the
 "Advanced Options: Regenerate Tiering with different parameters" panel and
 pressing the "Recalculate Tiering with new parameters" button. The tiering

06-conclusions.Rmd

@@ -15,7 +15,7 @@ This chapter will summarize:
 ## Key conclusions
 
 In this course you will have gained a better understanding of the current best
-practices for neoantigen indentification and prioritization. You will have
+practices for neoantigen identification and prioritization. You will have
 learned how to run pVACtools, interpret pVACtools results, and select neoantigen
 candidates suitable for vaccine manufacturing using pVACview.
 

resources/dictionary.txt

@@ -9,6 +9,7 @@ Bloomberg
 Bookdown
 bioinformatics
 biotype
+CDS
 CHROM
 CLI
 ClinVar
@@ -37,6 +38,7 @@ favicon
 frameshift
 fyi
 GDSCN
+GTF
 GenBank
 GH
 GitHub
@@ -66,6 +68,7 @@ indel
 indels
 inframe
 itcrtraining
+JunctionReadCount
 json
 junctional
 Leanpub
@@ -103,17 +106,20 @@ proteomics
 pVAC
 pVACfuse
 pVACseq
+pVACsplice
 pvactools
 pVACtools
 pVACvector
 pVACview
 pVACviz
 RefSeq
+RegTools
 reproducibility
 somatically
 subclonal
 summarization
 STARFusion
+SpanningFragCount
 tbi
 tiering
 TSL