Table 1 Common tools with their functions or characteristics in the neoantigen prediction process
From: Advances of mRNA vaccine in tumor: a maze of opportunities and challenges
Neoantigen prediction step | Common tools | The function or characteristics of tools |
|---|---|---|
Quality control of sequencing reading | FastQC [162] | To analyze samples with uncertain DNA sources or multiple sources |
ClinQC [163] | Quality control and modification of raw sequencing data generated by Sanger sequencing, Illumina, 454 and Ion Torrent sequencing | |
Lighter [164] | A commonly used and efficient tool for correcting sequencing errors | |
Musket [165] | An efficient correction tool for Illumina short-read data | |
SequencErr [166] | An emerging tool for evaluating, calibrating, and monitoring sequencer error rates | |
Read alignment | NovoAlign [167] | NGS aligner; high sensitivity towards short reads, long reads and complex genome; slow alignment; high percentage of properly paired reads |
BWA [167] | NGS aligner; low sensitivity towards short reads; fast alignment; high percentage of properly paired reads | |
Smalt [167] | NGS aligner; low sensitivity towards short reads; medium alignment speed; low percentage of proper pair in both short and long reads | |
Stampy [167] | NGS aligner; moderate sensitivity towards short reads; slow alignment; high percentage of proper pair in both short and long reads | |
Bowtie2 [167] | NGS aligner; low sensitivity towards short reads; medium alignment speed; low percentage of proper pair in both short and long reads | |
STAR [168] | A universal RNA-sequence aligner with superior mapping speed | |
Somatic mutation calling | VarScan 2 [169] | Discover SNVs and CNVs |
VarDict [170] | Discover SNV, MNV, InDels, complex and structural variants | |
SomaticSniper [171] | Discover somatic point mutations | |
MuTect [172] | Discover somatic point mutations with very low allele fractions | |
cn.MOPS [173] | Detection of CNVs | |
Manta [174] | Discover structural variants and indels | |
FusionMap [175] | Detect gene fusions from RNA-Sequence or gDNA-Sequence | |
HLA allele typing | PHLAT [40] | High accuracy at four-digit (92%-95%) and two-digit resolutions (96%-99%) |
OptiType [41] | High two-digit accuracy (97%), only serves for HLA class I typing | |
HLA-HD [176] | Determine with 6-digit precision | |
HLA-VBSeq [177] | Determine with 8-digit precision | |
Neoantigen prediction | NetMHCpan [178] | MHC-I binding prediction |
NetMHCIIpan [178] | MHC-II binding prediction | |
MHCflurry [44] | MHC I binding prediction; faster prediction than NetMHCpan | |
DeepHLA-pan [179] | Prediction of HLA-peptide binding (binding model) and the potential immunogenicity (immunogenicity model) of the peptide-HLA complex | |
NetCTL [180] | Prediction of proteasomal cleavage, TAP transport efficiency, and MHC I affinity | |
EDGE [52] | Prediction of HLA I and HLA II binding peptides | |
MARIA [53] | Prediction of HLA I and HLA II binding peptides | |
ATLAS [22] | Using patient’s T cell immune response machinery to identify optimal tumor-specific neoantigens |