These scripts assist in setting up files ofr manualling reviewing the results for Neoantigen Vaccine Desing results generate from the Washington University Immuno Pipeline.
A written case final report will be created which includes a Genomics Review Report document. This document includes a section of a basic data QC review and a table summarizing values that pass/fail the FDA quality thresholds.
Pull the basic data qc from various files. This script will output a file final_results/qc_file.txt and also print the summary to to screen.
mkdir $WORKING_BASE/../manual_review
cd $WORKING_BASE/../manual_review
bsub -Is -q oncology-interactive -G $GROUP -a "docker(griffithlab/neoang_scripts:version7)" /bin/bash
python3 /opt/scripts/get_neoantigen_qc.py -WB $WORKING_BASE -f final_results --yaml $WORKING_BASE/yamls/$CLOUD_YAML
This script will output a file final_results/fda_quality_thresholds_report.tsv and also print the summary to to screen.
python3 /opt/scripts/get_FDA_thresholds.py -WB $WORKING_BASE -f final_results
This script will output a file manual_review/hla_comparison.tsv and also print the summary to to screen.
python3 /opt/scripts/hla_comparison.py -WB $WORKING_BASE
exit
After the Immunogenomics Tumor Board Review, both a .tsv and .xlsx file are downloaded from pVACview whihc contains the canidates marked as Accept, Review, Reject, and Pending. These files should be kept in a fould named itb-review-files.
cd $WORKING_BASE
mkdir ../generate_protein_fasta
cd ../generate_protein_fasta
mkdir candidates
mkdir all
#generate a protein fasta file using the final annotated/evaluated neoantigen candidates TSV as input
#this will filter down to only those candidates under consideration and use the top transcript
# check the file to find Tumor sample ID in the #CHROM header of VCF
zcat $WORKING_BASE/final_results/annotated.expression.vcf.gz | less
export TUMOR_ID="100-049-BG004667"
bsub -Is -q general-interactive -G $GROUP -a "docker(griffithlab/pvactools:4.0.1)" /bin/bash
pvacseq generate_protein_fasta \
-p $WORKING_BASE/final_results/pVACseq/phase_vcf/phased.vcf.gz \
--pass-only --mutant-only -d 150 \
-s $TUMOR_ID \
--aggregate-report-evaluation {Accept,Review} \
--input-tsv ../itb-review-files/*.tsv \
$WORKING_BASE/final_results/annotated.expression.vcf.gz \
25 \
$WORKING_BASE/../generate_protein_fasta/candidates/annotated_filtered.vcf-pass-51mer.fa
pvacseq generate_protein_fasta \
-p $WORKING_BASE/final_results/pVACseq/phase_vcf/phased.vcf.gz \
--pass-only --mutant-only -d 150 \
-s $TUMOR_ID \
$WORKING_BASE/final_results/annotated.expression.vcf.gz \
25 \
$WORKING_BASE/../generate_protein_fasta/all/annotated_filtered.vcf-pass-51mer.fa
exit To generate files needed for manual review, save the pVAC results from the Immunogenomics Tumor Board Review meeting as $SAMPLE.revd.Annotated.Neoantigen_Candidates.xlsx (Note: if the file is not saved under this exact name the below command will need to be modified).
export PATIENT_ID=TWJF-5120-28
bsub -Is -q oncology-interactive -G $GROUP -a "docker(griffithlab/neoang_scripts:version7)" /bin/bash
cd $WORKING_BASE
mkdir ../manual_review
python3 /opt/scripts/generate_reviews_files.py -a ../itb-review-files/*.tsv -c ../generate_protein_fasta/candidates/annotated_filtered.vcf-pass-51mer.fa.manufacturability.tsv -variants final_results/variants.final.annotated.tsv -classI final_results/pVACseq/mhc_i/*.all_epitopes.aggregated.tsv -classII final_results/pVACseq/mhc_ii/*.all_epitopes.aggregated.tsv -samp $PATIENT_ID -o ../manual_review/
# Note: You can change the classI and classI IC50/percentile cutoff for coloring
python3 /opt/scripts/color_peptides51mer.py -p ../manual_review/*Peptides_51-mer.xlsx -probPos C -samp $PATIENT_ID -o ../manual_review/
A written case final report will be created which includes a Genomics Review Report document. This document includes a section of a basic data QC review and a table summarizing values that pass/fail the FDA quality thresholds.
Pull the basic data qc from various files. This script will output a file final_results/qc_file.txt and also print the summary to to screen.
cd $WORKING_BASE
docker run -it --env HOME --env WORKING_BASE -v $HOME/:$HOME/ -v $HOME/.config/gcloud:/root/.config/gcloud griffithlab/neoang_scripts:version7 /bin/bash
cd $WORKING_BASE
mkdir ../manual_review
cd ../manual_review
python3 /opt/scripts/get_neoantigen_qc.py -WB $WORKING_BASE -f final_results --yaml $HOME/yamls/${GCS_CASE_NAME}_immuno_cloud-WDL.yaml
python3 /opt/scripts/get_FDA_thresholds.py -WB $WORKING_BASE -f final_results
python3 /opt/scripts/hla_comparison.py -WB $WORKING_BASE
exit
After the Immunogenomics Tumor Board Review, both a .tsv and .xlsx file are downloaded from pVACview whihc contains the canidates marked as Accept, Review, Reject, and Pending. These files should be kept in a fould named itb-review-files.
cd $WORKING_BASE
mkdir ../generate_protein_fasta
cd ../generate_protein_fasta
mkdir candidates
mkdir all
#generate a protein fasta file using the final annotated/evaluated neoantigen candidates TSV as input
#this will filter down to only those candidates under consideration and use the top transcript
# check the file to find Tumor sample ID in the #CHROM header of VCF
gzcat $WORKING_BASE/final_results/annotated.expression.vcf.gz | less
export TUMOR_SAMPLE_ID="100-049-BG004667"
docker pull griffithlab/pvactools:4.0.5
docker run -it -v $HOME/:$HOME/ --env $WORKING_BASE --env SAMPLE_ID griffithlab/pvactools:4.0.5 /bin/bash
cd $WORKING_BASE
pvacseq generate_protein_fasta \
-p $WORKING_BASE/final_results/pVACseq/phase_vcf/phased.vcf.gz \
--pass-only --mutant-only -d 150 \
-s $TUMOR_SAMPLE_ID \
--aggregate-report-evaluation {Accept,Review} \
--input-tsv ../itb-review-files/*.tsv \
$WORKING_BASE/final_results/annotated.expression.vcf.gz \
25 \
$WORKING_BASE/../generate_protein_fasta/candidates/annotated_filtered.vcf-pass-51mer.fa
pvacseq generate_protein_fasta \
-p $WORKING_BASE/final_results/pVACseq/phase_vcf/phased.vcf.gz \
--pass-only --mutant-only -d 150 \
-s $TUMOR_SAMPLE_ID \
$WORKING_BASE/final_results/annotated.expression.vcf.gz \
25 \
$WORKING_BASE/../generate_protein_fasta/all/annotated_filtered.vcf-pass-51mer.fa
exit To generate files needed for manual review, save the pVAC results from the Immunogenomics Tumor Board Review meeting as $SAMPLE.revd.Annotated.Neoantigen_Candidates.xlsx (Note: if the file is not saved under this exact name the below command will need to be modified).
docker pull griffithlab/neoang_scripts
docker run -it --env WORKING_BASE --env PATIENT_ID -v $HOME/:$HOME/ -v $HOME/.config/gcloud:/root/.config/gcloud griffithlab/neoang_scripts:version7 /bin/bash
cd $WORKING_BASE
mkdir manual_review
python3 /opt/scripts/generate_reviews_files.py -a itb-review-files/*.tsv -c generate_protein_fasta/candidates/annotated_filtered.vcf-pass-51mer.fa.manufacturability.tsv -classI final_results/pVACseq/mhc_i/*.all_epitopes.aggregated.tsv -classII final_results/pVACseq/mhc_ii/*.all_epitopes.aggregated.tsv -samp $PATIENT_ID -o manual_review/
python3 /opt/scripts/color_peptides51mer.py -p manual_review/*Peptides_51-mer.xlsx -samp $PATIENT_ID -o manual_review/
Open colored_peptides51mer.html and copy the table into an excel spreadsheet. The formatting should remain. Utilizing the Annotated.Neoantigen_Candidates and colored Peptides_51-mer for manual review.
python3 /opt/scripts/get_neoantigen_qc.py --help
usage: get_neoantigen_qc.py [-h] [-WB WB] [-f FIN_RESULTS] [--n_dna N_DNA] [--t_dna T_DNA] [--t_rna T_RNA]
[--concordance CONCORDANCE] [--contam_n CONTAM_N] [--contam_t CONTAM_T]
[--rna_metrics RNA_METRICS] [--strand_check STRAND_CHECK] --yaml YAML
[--fin_variants FIN_VARIANTS]
Get the stats for the basic data QC review in the neoantigen final report.
optional arguments:
-h, --help show this help message and exit
-WB WB The path to the gcp_immuno folder of the trial you wish to run the script on, defined as
WORKING_BASE in envs.txt
-f FIN_RESULTS, --fin_results FIN_RESULTS
Name of the final results folder in gcp immuno
--n_dna N_DNA File path for aligned normal DNA FDA report table
--t_dna T_DNA File path for aligned tumor DNA FDA report table
--t_rna T_RNA File path for aligned tumor RNA FDA report table
--concordance CONCORDANCE
File path for Somalier results for sample tumor/normal sample relatedness
--contam_n CONTAM_N File path for VerifyBamID results for contamination of the normal sample
--contam_t CONTAM_T File path for VerifyBamID results for contamination of the tumor sample
--rna_metrics RNA_METRICS
File path for RNA metrics
--strand_check STRAND_CHECK
File path for strandness check
--yaml YAML File path for the pipeline YAML file
--fin_variants FIN_VARIANTS
File path for the final variants file
python3 /opt/scripts/get_FDA_thresholds.py --help
usage: get_FDA_thresholds.py [-h] [-WB WB] [-f FIN_RESULTS] [--n_dna N_DNA] [--t_dna T_DNA] [--t_rna T_RNA]
[--una_n_dna UNA_N_DNA] [--una_t_dna UNA_T_DNA] [--una_t_rna UNA_T_RNA]
[--somalier SOMALIER] [--contam_n CONTAM_N] [--contam_t CONTAM_T]
Get FDA qc stats from various files and determine if they pass or fail.
optional arguments:
-h, --help show this help message and exit
-WB WB the path to the gcp_immuno folder of the trial you wish to tun script on, defined as
WORKING_BASE in envs.txt
-f FIN_RESULTS, --fin_results FIN_RESULTS
Name of the final results folder in gcp immuno
--n_dna N_DNA file path for aligned normal dna FDA report table
--t_dna T_DNA file path for aligned tumor dna FDA report table
--t_rna T_RNA file path for aligned tumor rna FDA report table
--una_n_dna UNA_N_DNA
file path for unaligned normal dna FDA report table
--una_t_dna UNA_T_DNA
file path for unaligned tumor dna FDA report table
--una_t_rna UNA_T_RNA
file path for unaligned tumor rna FDA report table
--somalier SOMALIER file path for Somalier results for sample tumor/normal sample relatedness
(concordance.somalier.pairs.tsv)
--contam_n CONTAM_N file path for VerifyBamID results for contamination the normal sample
--contam_t CONTAM_T file path for VerifyBamID results for contamination the tumor dna sample
python3 /opt/scripts/hla_comparison.py --help
usage: hla_comparison.py [-h] [-WB WB] [-f FIN_RESULTS] [--optitype_n OPTITYPE_N] [--optitype_t OPTITYPE_T]
[--phlat_n PHLAT_N] [--phlat_t PHLAT_T] [--clinical CLINICAL] [--o O]
Compare HLA alleles called by phlat, opitype, and clincal data if available.
optional arguments:
-h, --help show this help message and exit
-WB WB The path to the gcp_immuno folder of the trial you wish to run the script on, defined as
WORKING_BASE in envs.txt
-f FIN_RESULTS, --fin_results FIN_RESULTS
Name of the final results folder in gcp immuno
--optitype_n OPTITYPE_N
File path for optitype normal calls
--optitype_t OPTITYPE_T
File path for optitype tumor calls
--phlat_n PHLAT_N File path for phlat normal calls
--phlat_t PHLAT_T File path for phlat tumor calls
--clinical CLINICAL File path for the clinical_calls.txt
--o O Output folder
python3 /opt/scripts/generate_reviews_files.py --help
usage: generate_reviews_files.py [-h] -a A -c C [-variants VARIANTS] -classI CLASSI -classII CLASSII -samp SAMP
[-o O] [-f FIN_RESULTS]
Create the file needed for the neoantigen manuel review
optional arguments:
-h, --help show this help message and exit
-a A The path to the ITB Reviewed Candidates tsv file
-c C The path to candidates annotated_filtered.vcf-pass-51mer.fa.manufacturability.tsv from
the generate_protein_fasta script
-variants VARIANTS The path to the variants.final.annotated.tsv file generated by the pipeline
-classI CLASSI The path to the classI all_epitopes.aggregated.tsv used in pVACseq
-classII CLASSII The path to the classII all_epitopes.aggregated.tsv used in pVACseq
-samp SAMP The name of the sample
-o O the path to output folder
-f FIN_RESULTS, --fin_results FIN_RESULTS
Name of the final results folder in gcp immuno
python3 /opt/scripts/color_peptides51mer.py --help
usage: color_peptides51mer.py [-h] -p P -samp SAMP [-cIIC50 CIIC50] [-cIpercent CIPERCENT] [-cIIIC50 CIIIC50]
[-cIIpercent CIIPERCENT] [-probPos [PROBPOS [PROBPOS ...]]] [-o O]
Color the 51mer peptide
optional arguments:
-h, --help show this help message and exit
-p P The path to the Peptides 51 mer
-samp SAMP The name of the sample
-cIIC50 CIIC50 Maximum classI IC50 score to annotate
-cIpercent CIPERCENT Maximum classI percentile to annotate
-cIIIC50 CIIIC50 Maximum classII IC50 score to annotate
-cIIpercent CIIPERCENT
Maximum classII percentile to annotate
-probPos [PROBPOS [PROBPOS ...]]
problematic position to make large
-o O the path to output folder