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Table 1 The summary of neoantigen prediction software

From: Neoantigen vaccine: an emerging tumor immunotherapy

Software Principle Year
HLAminer [52] Based on the shotgun sequencing database from Illumina platform, the HLA type was predicted by orienting the assembly of shotgun sequence data and comparing it with the reference allele sequence database 2012
VariantEffect Predictor Tool [53] Automate annotations in a standard way to reduce manual review time, annotate and prioritize variants 2016
NetMHCpan [54] Sequence comparison method based on artificial neural network, and predict the affinity of peptide-MHC-I type molecular 2016
UCSC browser [55] Based on sequence search, the fusion of multiple databases can provide fast and accurate access to any gene segment 2002
CloudNeo pipeline [56] Docker container was used to complete the tasks in the workflow. After the mutant VCF file and bam file representing HLA typing were input respectively, the HLA affinity prediction of all mutant peptides was obtained 2017
OptiType [57] The HLA typing algorithm based on integer linear programming provides sequencing databases including RNA, exome and whole genome 2014
ATHLATES [58] Assembly, allele recognition and allele pair inference were applied to short sequences, and the HLA genotyping at allele level was achieved by exon sequencing 2013
pVAC-Seq [59] To integrate tumor mutation and expression data and identify personalized mutagens by tumor sequencing 2016
MuPeXI [60] The extraction and induction of mutant peptides can roughly identify tumor-specific peptides, predict their immunogenicity, and evaluate their potential for new epitopes 2017
Strelka [61] Based on a new Bayesian model, the matching tumor-normal sample sequencing data was used to analyze and predict somatic cell variation, with high accuracy and sensitivity 2012
Strelka2 [62] Based on the mixed model, the error parameters of each sample insertion or deletion were estimated, and the liquid tumor analysis was improved 2018
VarScan2 [63] Somatic and copy number mutations in tumor-normal exome data were detected by heuristic statistical algorithm 2012
Somaticseq [64] Based on a randomized enhancement algorithm, more than 70 individual genome and sequencing features were extracted for each candidate site to accurately detect somatic mutations 2015
SMMPMBEC [65] Using matrix as a Bayesian prior, based on the optimal neural network predicting peptide with MHC-I type molecules 2009
NeoPredPipe [66] Based on a pipeline connecting commonly used bioinformatic software via custom python scripts to provide neoantigen burden, tumor heterogeneity, immune stimulation potential and HLA haplotype of patients 2019