Table 1 Computational tools used in tumor immunogenomics with high-throughput next-generation sequencing data
Tool | Characteristics | URL | Year | Ref. |
|---|---|---|---|---|
Quantification of immune cells in TIME | ||||
CIBERSORT | Based on linear support vector regression, deconvolution from microarray data, and known gene expression profiling set. | 2015 | ||
CIBERSORTx | Expanding data source to single-cell RNA-seq. | 2019 | ||
DeconRNASeq | Constrained least square regression model validated with RNA-seq data from five human tissues | 2013 | ||
EPIC | Based on constrained least square, incorporates a non-negative condition into deconvolution. | 2017 | ||
ESTIMATE | Generating a stromal score and immune score to reflect the tumor purity based on ssGSEA | 2013 | ||
FARDEEP | Based on adaptive least trimmed square, FARDEEP removes outliers and outputs the absolute quantification of cell types | 2019 | ||
MCP-counter | The score is the geometric mean of the expression level of cell-specific genes, implying the absolute abundance of immune cell types among samples | 2016 | ||
MuSiC | Deconvolution of bulk sequencing data based on cell type-specific gene expression reference from single-cell RNA-seq | 2019 | ||
NITUMD | A semi-supervised nonnegative matrix factorization framework with a trichotomous signature matrix | 2020 | ||
PERT | A non-negative maximum likelihood-based method applied to fresh human umbilical cord blood samples | – | 2012 | |
quanTIseq | Quantification of ten different immune cell types and other uncharacterized cells based on RNA-seq data | 2019 | ||
TIMER | Immune cells are estimated via transcriptomic data and the correlations among the immunological, genomic, and clinical features were established. | 2016 | ||
xCell | Spillover compensation is used to separate cell types with high correlation | 2017 | ||
Prediction of mutated proteins | ||||
CN-Learn | Machine-learning framework integrating calls from multiple CNV detection algorithms and learning to accurately identify true CNVs | 2019 | ||
deepSNV | Detecting and quantifying sub-clonal SNVs in mixed populations even for low-frequency variants | 2012 | ||
DeepVariant | SNP and small-indel variant caller using deep neural networks in aligned NGS read data | 2018 | ||
EBCall | Discriminating somatic mutations from sequencing errors with both moderate and low allele frequencies | 2013 | ||
GATK | Industry standard for identifying SNPs and indels via analyzing WES, WGS, and RNA-seq data | 2010 | ||
LoFreq | Modeling sequencing run-specific error rates to accurately call variants occurring in <0.05% of a population | 2012 | ||
MuTect2 | Sensitive detection of somatic point mutations especially in low-allelic-fraction events | 2013 | ||
Platypus | Using local de novo assembly to generate candidate variants, including SNPs, indels, and complex polymorphisms | 2014 | ||
PyroHMMsnp | Realigning read sequences around homopolymers and inferring the underlying genotype by using a Bayesian approach | 2013 | ||
SAMtools | Variant caller utilizing the post-processing alignments in the SAM/BAM format | 2009 | ||
SCcaller | Firm foundation for standardized somatic-mutation analysis in single-cell genomics based on single-cell multiple displacement amplification (SCMDA) | 2017 | ||
SomaticSeq | Somatic mutation detection pipeline used to produce highly accurate somatic mutation calls for both SNVs and small INDELs | 2015 | ||
SomaticSniper | Calling of somatic SNPs and indels from matched tumor–normal NGS data | 2011 | ||
Strelka2 | Fast and accurate caller of germline and somatic variants based on Strelka | 2018 | ||
VarDict | Variant caller of SNV, MNV, INDELs, and SVs, enabling ultra-deep sequencing | 2016 | ||
VarScan2 | Detection of somatic mutations and CNVs in exome data from tumor–normal pairs | 2012 | ||
HLA typing | ||||
HISAT2 | Graph-based genome alignment and genotyping, also applied for DNA fingerprinting | 2019 | ||
HLA-HD | Extraction of six-digit resolution HLA-I and HLA-II from NGS data | 2017 | ||
HLA-miner | HLA-I and HLA-II typing directly from non-targeted RNA-seq, WGS and WES data | 2012 | ||
HLAProfiler | K-mer profile-based method for HLA calling in RNA-seq data for both rare and common HLA alleles at two-field precision | 2017 | ||
HLAreporter | Extraction of HLA-I and HLA-II from NGS data at four-digit resolution | 2015 | ||
HLAscan | Determination of HLA type across the whole-genome, exome, and target sequences | 2017 | ||
HLAssign | First highly automated open-source HLA-typing method for NGS data to three-field resolution | https://www.ikmb.uni-kiel.de/resources/download-tools/software/hlassign | 2015 | |
HLA-VBseq | Genotyping of HLA alleles at an 8-digit resolution from WGS data without the need of prior knowledge regarding the HLA loci | 2015 | ||
Kourami | Graph-guided assembly technique used to provide highly classical HLA typing | 2018 | ||
Optitype | Genotyping of major and minor HLA-I alleles from RNA-seq, WGS, and WES data not specifically enriched for the HLA cluster | 2014 | ||
PHLAT | High-accuracy genotyping of HLA-I and HLA-II alleles from RNA-seq, WGS, WES, and targeted sequencing at a four-digit resolution | 2014 | ||
Polysolver | High-precision HLA typing of WES data even relatively low-coverage WES data, and subsequent mutation detection | 2015 | ||
seq2HLA | Using standard RNA-Seq reads as input to determine the HLA-I and HLA-II types and expression at a four-digit resolution | 2012 | ||
SNP2HLA | Imputing four-digit classical alleles and amino acid polymorphisms at class I and class II loci | 2013 | ||
Prediction of antigen-MHC binding affinity | ||||
ACME | Pan-specific peptide–MHC class I binding prediction through attention-based deep neural networks | 2019 | ||
MHCAttnNet | MHC-peptide binding prediction of MHC alleles classes I and II using an attention-based deep neural model | 2020 | ||
MHCflurry | Open-source class I MHC binding affinity prediction, using mass spectrometry datasets for model selection and showing competitive accuracy | 2018 | ||
MHCSeqNet | Open-source deep neural network model for universal MHC binding prediction, accepting peptides of any length | 2019 | ||
NetMHC | High accuracy prediction of pMHC binding affinity to human and non-human MHC-I molecules based on ANN and PSSMs | 2008 | ||
NetMHCII | High accuracy prediction of pMHC binding affinity to human and non-human MHC-II molecules based on ANN and PSSMs | 2018 | ||
NetMHCIIpan | Pan-specific version of netMHCII | 2020 | ||
NetMHCpan | Pan-specific version of netMHC | 2020 | ||
PSSMHCpan | PSSM based software for predicting class I peptide-HLA binding affinity | 2017 | ||
PUFFIN | Deep residual network-based computational approach that quantifies uncertainty in pMHC affinity prediction | 2019 | ||
