bio-workflows-neoantigen-pipeline

Tumor neoantigen identification processes somatic VCF data through HLA typing, MHC binding predictions, and pVACtools neoantigen calling to rank vaccine candidates. It integrates immunogenicity scoring across multiple HLA alleles to support personalized cancer immunotherapy and biomarker discovery.

MHC binding prediction for neoantigen ranking requires somatic mutation VCFs, HLA typing results, VEP annotation, and expression data with VAF information. Tools needed include pVACtools alongside mhcflurry or NetMHCpan to compute robust candidate rankings.

Yes, pVACtools neoantigen calling supports integration with mhcflurry and NetMHCpan for MHC binding predictions. The workflow orchestrates these prediction algorithms alongside HLA typing and immunogenicity scoring to produce ranked vaccine candidates.

The best end-to-end neoantigen discovery workflow orchestrates HLA typing, MHC binding prediction, pVACtools calling, and immunogenicity scoring in sequence. Apply this pipeline to tumor somatic mutations to rank vaccine candidates across multiple HLA alleles for personalized immunotherapy.

VEP annotation and expression data are needed for neoantigen ranking to validate somatic mutations and assess variant allele frequency. This information ensures robust immunogenicity scoring and accurate vaccine candidate prioritization across HLA alleles.

HLA typing integration supports multiple alleles for personalized immunotherapy by calculating MHC binding predictions across diverse HLA types. The workflow ranks neoantigen vaccine candidates specifically for individual patient immunogenicity profiles.