Table 1.

Computational tools and pipelines used in/for neoantigen prediction

Purpose	Name	Input data	HLA class	Repository (if available)
HLA typing tools	OptiType92	WGS/WES/RNA-seq	Class I	https://github.com/FRED-2/OptiType
	PolySolver93	WES	Class I	https://github.com/jason-weirather/hla-polysolver
	HLA-HD94	WGS/WES/RNA-seq	Class I and II	https://www.genome.med.kyoto-u.ac.jp/HLA-HD/
	HISAT-genotype95	WGS/WES/RNA-seq	Class I and II	https://daehwankimlab.github.io/hisat-genotype/
	arcasHLA159	WGS/WES/RNA-seq	Class I and II	https://github.com/RabadanLab/arcasHLA
	HLAscan160	WGS/WES	Class I and II	https://github.com/SyntekabioTools/HLAscan
	xHLA161	WGS/WES	Class I and II	https://github.com/humanlongevity/HLA
	seq2HLA162	RNA-seq	Class I and II	https://github.com/TRON-Bioinformatics/seq2HLA
	PHLAT163	WGS/WES/RNA-seq	Class I and II	https://sites.google.com/site/phlatfortype/home
	ATHLATES164	WGS/WES/amplicon	Class I and II	https://github.com/cliu32/athlates
	HLA-VBSeq165	WGS	Class I and II	http://nagasakilab.csml.org/hla/
	HLAminer166	WGS/WES/RNA-seq/amplicon	Class I and II	https://github.com/bcgsc/HLAminer
	HLA-LA167	WGS/WES/RNA-seq	Class I and II	https://github.com/DiltheyLab/HLA-LA

	Name	Specificity	Method	Input data	HLA class	Repository (if available)
Binding affinity prediction tools	MHCflurry107	Allele	ANN	Peptide sequence (individual peptides or multi-FASTA format) and MHC alleles	Class I	https://github.com/openvax/mhcflurry
	MHCflurry 2.099	Pan	ANN		Class I	https://github.com/openvax/mhcflurry
	NetMHC106	Allele	ANN		Class I	https://services.healthtech.dtu.dk/service.php?NetMHC-4.0
	NetMHCpan110	Pan	ANN		Class I	http://www.cbs.dtu.dk/services/NetMHCpan/
	mixMHCpred	Allele	ANN		Class I	https://github.com/GfellerLab/MixMHCpred
	MHCSeqNet168	Pan	ANN		Class I	https://github.com/cmb-chula/MHCSeqNet
	PickPocket169	Pan	SMM		Class I	http://www.cbs.dtu.dk/services/PickPocket/
	IEDB smm170	Allele	SMM		Class I	http://tools.iedb.org/mhci/
	NetMHCcons111	Pan	consensus		Class I	http://www.cbs.dtu.dk/services/NetMHCcons/
	DeepSeqPan171	Pan	DCNN		Class I	https://github.com/pcpLiu/DeepSeqPan
	HLAthena172	Pan	ANN		Class I	http://hlathena.tools/
	PRIME133	Allele	GLM		Class I	https://github.com/GfellerLab/PRIME
	SHERPA117	Pan	GBDT		Class I	https://www.personalis.com/immunoid-next-platform/
	NetMHCIIpan110	Pan	ANN		Class II	http://www.cbs.dtu.dk/services/NetMHCIIpan/
	mixMHC2pred109	Allele	ANN		Class II	https://github.com/GfellerLab/MixMHC2pred
	MARIA173	Pan	ANN		Class II	https://maria.stanford.edu/about.php
	MHCNuggets174	Pan	ANN		Class I and II	https://github.com/KarchinLab/mhcnuggets

	Name	Neoantigen types	Input data	Neoantigen class	Repository (if available)
Neoantigen prediction pipelines	NextNEOpi98	SNVs, indels, gene fusions	WES/WGS and RNA-seq or WES/WGS only, as raw FASTQ or BAM files	Class I and II	https://github.com/icbi-lab/nextNEOpi
	NeoFuse175	Gene fusions	RNA-seq FASTQ files	Class I and II	https://github.com/icbi-lab/NeoFuse
	Antigen.garnish140	SNVs, indels, gene fusions	VCF of mutations, gene fusions, or transcripts or peptide sequences	Class I and II	https://github.com/andrewrech/antigen.garnish
	CloudNeo176	SNVs	VCF of somatic mutations and BAM (DNA- or RNA-seq)	Class I	https://github.com/TheJacksonLaboratory/CloudNeo
	DeepHLApan177	SNVs	CSV files	Class I	https://github.com/jiujiezz/deephlapan
	Epidisco178	SNVs, indels, splice variants, gene fusions	WES and RNA-seq FASTQ files	Class I	https://github.com/hammerlab/epidisco
	INTEGRATE-neo179	Gene fusions	RNA-seq or WGS FASTQ files	Class I	https://github.com/ChrisMaherLab/INTEGRATE-Neo
	MuPeXI180	SNVs, indels	VCF of somatic mutations and precomputed expression data	Class I	https://github.com/ambj/MuPeXI
	Neoantimon181	SNVs, indels, structural variants	VCF of somatic mutations or file of mutant RNA sequences, and precomputed HLA types	Class I and II	https://github.com/hase62/Neoantimon
	neoANT-HILL182	SNVs, indels	VCF of somatic mutations, RNA-seq data (BAM or FASTQ files)	Class I	https://github.com/neoanthill/neoANT-HILL
	NeoFlow183	SNVs, indels	VCF of somatic mutations, DNA- or RNA-seq FASTQ files, MS data in MGF format	Class I	https://github.com/bzhanglab/neoflow
	Neopepsee126	SNVs	VCF of somatic mutations, RNA-seq FASTQ files, and HLA types	Class I	https://sourceforge.net/p/neopepsee/wiki/Home/
	NeoPredPipe184	SNVs, indels	VCF of somatic mutations and HLA types	Class I and II	https://github.com/MathOnco/NeoPredPipe
	NeoepitopePred	SNVs, gene fusions	WGS FASTQ files or WGS, WES or RNA-seq BAM files	Class I	https://stjudecloud.github.io/docs/guides/genomics-platform/analyzing-data/neoepitope/
	Neoepiscope185	SNVs, indels	VCF of somatic mutations, mapped DNA-seq reads (BAM), and HLA alleles	Class I and II	https://github.com/pdxgx/neoepiscope
	nf-core/epitopeprediction186	SNVs, indels	VCF of somatic mutations	Class I and II	https://github.com/nf-core/epitopeprediction
	OpenVax187	SNVs	FASTQ from WES and RNA-seq	Class I
	ProGeo-neo188	SNVs	VCF of somatic mutations, RNA-seq FASTQ files	Class I	https://github.com/kbvstmd/ProGeo-neo
	ProTECT189	SNVs	DNA- and RNA-seq FASTQ files. Alternatively, precomputed BAM and/or VCF files	Class I and II	https://github.com/BD2KGenomics/protect
	pTuneos190	SNVs, indels	FASTQ from WES and RNA-seq. Alternatively, VCF of somatic mutations, expression data, copy number, and tumor cellularity information	Class I	https://github.com/bm2-lab/pTuneos
	pVACtools191	SNVs, indels, gene fusions	VCF of somatic mutations, expression/coverage information from DNA- and RNA-seq (pVACseq), gene fusions (pVACfuse), and HLA types.	Class I and II	https://github.com/griffithlab/pVACtools
	ScanNeo192	Indels	Mapped RNA-seq reads (BAM)	Class I	https://github.com/ylab-hi/ScanNeo
	TIminer193	SNVs	VCF of somatic mutations, RNA-seq FASTQ files	Class I	https://icbi.i-med.ac.at/software/timiner/timiner.shtml
	TSNAD194	SNVs, indels	WES FASTQ files	Class I	https://github.com/jiujiezz/tsnad
	Vaxrank195	SNVs, indels	VCF of somatic mutations, mapped RNA-seq reads (BAM), and HLA types	Class I	https://github.com/openvax/vaxrank
	TruNeo196	SNVs, indels	WES and RNA-seq FASTQ files	Class I	https://github.com/yucebio/TruNeo

ANN, artificial neural network; CSV, comma separated values; DCNN, deep convoluted neural network; HLA, human leukocyte antigen; GBDT, gradient boosted decision trees; GLM, generalized linear model; MGF, mascot generic format; MHC, major histocompatibility complex; MS, mass spectrometry; RNA-seq, RNA sequencing; SMM, stabilized matrix method; SNVs, single nucleotide variations; VCF, variant call format; WES, whole exome sequencing; WGS, whole genome sequencing.