From: A review of deep learning applications in human genomics using next-generation sequencing data
Tools | DL model | Application | Input/Output | Website Code Source | References |
---|---|---|---|---|---|
DanQ | CNN + BLSTM | To predict DNA function directly from sequence data | .mat /.mat | [152] | |
SPEID | CNN + LSTM | For enhancer–promoter interaction (EPI) prediction | .mat /.mat | [153] | |
EP2vec | NLP + GBRT | To predict enhancer–promoter interactions (EPIs) | CSV / CSV | [154] | |
D-GEX (deep learning for gene expression) | FNN | To understand the expression of target genes from the expression of landmark genes | .cel, txt, BAM / txt | [155] | |
DeepExpression | CNN | To predict gene expression using promoter sequences and enhancer–promoter interactions | .txt /.txt | [156] | |
DeepGSR | CNN + ANN | To recognise various types of genomic signals and regions (GSRs) in genomic DNA (e.g. splice sites and stop codon) | FASTA /.txt | [157] | |
SpliceAI | CNN | To identify splice function from pre-mRNA sequencing | VCF / VCF | [71] | |
SpliceRover | CNN | For splice site prediction | FASTA /.txt | N/A | [158] |
Splice2Deep | CNN | For splice site prediction in Genomic DNA | FASTA /.txt | [29] | |
DeepBind | CNN | To characterise DNA- and RNA-binding protein specificity | FASTA /.txt | [111] | |
Gene2vec | NLP | To produce a representation of genes distribution and predict gene–gene interaction | .txt /.txt | [130] | |
MPRA-DragoNN | CNN | To predict and analyse the regulatory DNA sequences and non-coding genetic variants | N/A | [77] | |
BiRen | CNN + GRU + RNN | For enhancers predictions | BED, BigWig /CSV | [159] | |
APARENT (APA REgression NeT) | CNN | To predict and engineer the human 3' UTR Alternative Polyadenylation (APA) and annotate pathogenetic variants | FASTA / CSV | [72] | |
LaBranchoR (LSTM Branchpoint Retriever) | BLSTM | To predict the location of RNA splicing branchpoint | FASTA / FASTA | [160] | |
COSSMO | CNN, BLSTM + ResNet | To predict the splice site sequencing and splice factors | TSV, CSV /CSV | [79] | |
Xpresso | CNN | To predict gene expression levels from genomic sequence | FASTA /.txt | [73] | |
DeepLoc | CNN + BLSTM | To predict subcellular localisation of protein from sequencing data | FASTA/ prediction score | [161] | |
SPOT-RNA | CNN | To predict RNA Secondary Structure | FASTA /.bpseq,.ct, and.prob | [162] | |
DeepCLIP | CNN + BLSTM | For predicting the effect of mutations on protein–RNA binding | FASTA /.txt | [163] | |
DECRES (DEep learning for identifying Cis-Regulatory ElementS) | MLP + CNN | To predict active enhancers and promoters across the human genome | FASTA /.txt | [74] | |
DeepChrome | CNN | For prediction of gene expression levels from histone modification data | Bam / TSV | [164] | |
DARTS | DNN + BHT | Deep learning augmented RNA-seq analysis of transcript splicing | .txt |  |