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https://github.com/griffithlab/neoag_vaccine_scripts


https://github.com/griffithlab/neoag_vaccine_scripts

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README 

          
# Neoantigen Pipeline Helper Scripts

These scripts assist in setting up files ofr manualling reviewing the results for Neoantigen Vaccine Desing results generate from the [Washington University Immuno Pipeline](https://github.com/wustl-oncology/analysis-wdls).

  

## Creating Case Final Report on compute 1



### Before Immunogenomics Tumor Board 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.



### Basic data QC



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

```



### FDA Quality Thresholds



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

```



### HLA Comparison

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 Immunogenomics Tumor Board Review



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.



#### Generate Protein Fasta



```bash

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/

```



## Creating Case Final Report locally



### Before Immunogenomics Tumor Board 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.



### Basic data QC



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 Immunogenomics Tumor Board Review



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.



#### Generate Protein Fasta



```bash

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.



# Description of Scripts



## Get Basic QC



```

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

```



## GET FDA metrics



```

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

```



## HLA Comparison

```

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

```



## Generate Review Files



```

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

```



## Color Peptides 51mer



```

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

```