Merge branch 'master' into extended-pepetide-sheet

Author: evelyn-schmidt

ModifiedPeptides.md

@@ -187,7 +187,7 @@ cat */pvacbind_results/*/MHC_Class_I/${SAMPLE_NAME}.all_epitopes.tsv | grep -v "
 ```
 
 ### Evaluate the proposed modified peptide sequences
-The goal of this analysis is to test whether any strong binding peptides are created that include the modified amino acid sequences included to improve solubility. For example, one could require that no such peptides exist where the median binding affinity is < 500nm OR median binding score percentile is < 1%.
+The goal of this analysis is to test whether any strong binding peptides are created that include the modified amino acid sequences included to improve solubility. For example, one could require that no such peptides exist where the median binding affinity is < 500nm OR median binding score percentile is < 2%.
 
 For each candidate modified peptide sequence, summarize the number of such potentially problematic peptides. 
 

README.md

@@ -75,12 +75,14 @@ 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).
 
 ```
-cd $WORKING_BASE/../manual_review
-bsub -Is -q oncology-interactive -G $GROUP -a "docker(griffithlab/neoang_scripts)" /bin/bash
+docker pull griffithlab/neoang_scripts
+docker run -it --env WORKING_BASE -v $HOME/:$HOME/ -v $HOME/.config/gcloud:/root/.config/gcloud griffithlab/neoang_scripts /bin/bash
 
-export SAMPLE="TWJF-10146-0029"
+cd $WORKING_BASE
 
-python3 /opt/scripts/setup_review.py -WB $WORKING_BASE -a ../itb-review-files/*.xlsx -c $WORKING_BASE/../generate_protein_fasta/candidates/annotated_filtered.vcf-pass-51mer.fa.manufacturability.tsv -samp $SAMPLE  -classI $WORKING_BASE/final_results/pVACseq/mhc_i/*.all_epitopes.aggregated.tsv -classII $WORKING_BASE/final_results/pVACseq/mhc_ii/*.all_epitopes.aggregated.tsv 
+python3 /opt/scripts/generate_reviews_files.py -a itb-review-files/*.xlsx -c generate_protein_fasta/candidates/annotated_filtered.vcf-pass-51mer.fa.manufacturability.tsv -classI gcp_immuno/final_results/pVACseq/mhc_i/*.all_epitopes.aggregated.tsv -classII gcp_immuno/final_results/pVACseq/mhc_ii/*.all_epitopes.aggregated.tsv -samp $GCS_CASE_NAME -o manual_review/
+
+python3 /opt/scripts/color_peptides51mer.py -p manual_review/*Peptides_51-mer.xlsx -samp $GCS_CASE_NAME -o manual_review/
 ```
 
 ## Creating Case Final Report locally

scripts/modify_peptides.py

@@ -32,7 +32,8 @@ def generate_modifed_peptides(n, name, base_sequence):
     characters = ['K', 'R']
 
     # Generate all possible combinations up to length n
-    all_combinations = chain.from_iterable(product(characters, repeat=i) for i in range(1, n))
+    all_combinations = chain.from_iterable(product(characters, repeat=i) for i in range(1, n+1))
+
     possible_modifications = set(''.join(combination) for combination in all_combinations)
 
     peptide_table = []
@@ -74,13 +75,13 @@ def assign_unique_numbers(df, column_name):
 def main():
     args = parse_arguments()
 
-    max_length = args.n
+    max_length = int(args.n)
 
     peptides = pd.read_csv(args.m, names=["Name", "Sequence"], header=None)
     peptides = peptides[1:]
     peptides = assign_unique_numbers(peptides, "Name")
 
-    max_length = 3
+    # max_length = 3
     list = []
 
     for index, row in peptides.iterrows():