Table 3 Pipelines for neoantigen prediction
From: The perfect personalized cancer therapy: cancer vaccines against neoantigens
Title | Input | Notes | Date | Ref |
|---|---|---|---|---|
TIminer: NGS data mining pipeline for cancer mmunology and immunotherapy. | RNA-seq BAM and VCF | Computes GSEA and IPS | 10/2017 | [64] |
CloudNeo: a cloud pipeline for identifying patient-specific tumor neoantigens. | BAM for HLA and VCF | Computes HLA type and Neoantigens | 10/2017 | [65] |
TSNAD: an integrated software for cancer somatic mutation and tumour-specific neoantigen detection. | FASTQ; BAM for HLA | Neoantigen detection pipeline | 05/2017 | [66] |
INTEGRATE-neo: a pipeline for personalized gene fusion neoantigen discovery. | FASTQ | Gene fusion prediction and neoantigen computation from gene fusions | 02/2017 | [67] |
pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens. | prepare FASTA (prepare input) and predicts neoantigens | Neoantigen calling, HLA typing, MHC binding | 01/2016 | [68] |
neoantigenR: An annotation based pipeline for tumor neoantigen identification from sequencing data | GSS + FASTA | R package, uses MHC, unpublished |  | [43] |