bio-workflows-neoantigen-pipeline

name	bio-workflows-neoantigen-pipeline
description	End-to-end neoantigen discovery from somatic variants to ranked vaccine candidates. Integrates HLA typing, MHC binding prediction, pVACtools neoantigen calling, and immunogenicity scoring. Use when identifying tumor neoantigens for personalized vaccine design or checkpoint biomarkers.
tool_type	mixed
primary_tool	pVACtools
workflow	true
depends_on	["clinical-databases/hla-typing","immunoinformatics/mhc-binding-prediction","immunoinformatics/neoantigen-prediction","immunoinformatics/immunogenicity-scoring","immunoinformatics/epitope-prediction"]
qc_checkpoints	[{"after_hla":"HLA types resolved to 4-digit, coverage adequate"},{"after_binding":"Predictions generated for all alleles, IC50 <500nM filter"},{"after_neoantigen":"Neoantigens identified with VAF >0.1, expressed"},{"after_scoring":"Top candidates prioritized by immunogenicity"}]

Neoantigen Pipeline

Complete workflow from somatic variants to ranked neoantigen vaccine candidates for personalized cancer immunotherapy.

Workflow Overview

Somatic VCF (annotated) + Tumor RNA-seq (optional)
        |
        v
[1. HLA Typing] --> arcasHLA / OptiType (if types not provided)
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        v
[2. MHC Binding Prediction] --> MHCflurry / NetMHCpan
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        v
[3. Neoantigen Calling] --> pVACseq
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        v
[4. Immunogenicity Scoring] --> Multi-factor ranking
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        v
Ranked Vaccine Candidates (TSV + visualizations)

Prerequisites

pip install pvactools mhcflurry vatools

mhcflurry-downloads fetch

conda install -c bioconda vep arcashla optitype

Primary Path: pVACseq Pipeline

Step 1: HLA Typing (if not provided)

HLA types are critical for MHC binding prediction. If not already known from clinical testing:

# From tumor RNA-seq BAM
arcasHLA extract tumor.bam -t 8 -o hla_output/
arcasHLA genotype hla_output/tumor.extracted.1.fq.gz hla_output/tumor.extracted.2.fq.gz \
    -g A,B,C,DRB1,DQB1,DPB1 -t 8 -o hla_output/

# Parse results
cat hla_output/tumor.genotype.json

import json

with open('hla_output/tumor.genotype.json') as f:
    hla_data = json.load(f)

hla_alleles = []
for gene, alleles in hla_data.items():
    for allele in alleles:
        hla_alleles.append(f'HLA-{allele}')

# Format for pVACseq: HLA-A*02:01,HLA-A*24:02,HLA-B*07:02,...
hla_string = ','.join(hla_alleles)
print(f'HLA alleles: {hla_string}')

Step 2: VCF Annotation with VEP

pVACseq requires VEP-annotated VCF with specific fields:

# Annotate somatic VCF
vep --input_file somatic.vcf \
    --output_file somatic.vep.vcf \
    --format vcf --vcf --symbol --terms SO \
    --plugin Frameshift --plugin Wildtype \
    --offline --cache \
    --pick --fork 4


vcf-expression-annotator somatic.vep.vcf \
    expression.tsv gene \
    -s tumor_sample \
    -o somatic.vep.expression.vcf