Table 4 Genomic tools/algorithm based on deep learning architecture for epigenomics

DeepSEA

CNN

To predict multiple chromatin effects of DNA sequence alterations

N/A

https://github.com/Team-Neptune/DeepSea

[165]

FactorNet

CNN + RNN

For predict the cell-type specific transcriptional binding factors (TF)

BED / BED, gzipped bedgraph file

https://github.com/uci-cbcl/FactorNet

[120]

DeMo (Deep Motif Dashboard)

CNN + RNN

For transcription factor binding site perdition (TFBS) by classification task

FASTA / txt

https://github.com/const-ae/Neural_Network_DNA_Demo

[166]

DeepCpG

CNN + GRU

To predict the methylation states from single-cell data

TSV / TSV

https://github.com/cangermueller/deepcpg

[83]

DeepHistone

CNN

To accurately predict histone modification sites based on sequences and DNase-Seq (experimental) data

txt, CSV / CSV

https://github.com/ucrbioinfo/DeepHistone

[84]

DeepTACT

CNN

To predict 3D chromatin interactions

CSV / CSV

https://github.com/liwenran/DeepTACT

[167]

Basenji

CNN

To predict cell-type-specific epigenetic and transcriptional profiles in large mammalian genomes

FASTA / VCF

https://github.com/calico/basenji

[114]

Deopen

CNN

To predict the chromatin accessibility from DNA sequence/ Downstream analysis also included QTL analysis

BED, hkl /hkl

https://github.com/kimmo1019/Deopen

[31]

DeepFIGV (Deep Functional Interpretation of Genetic Variants)

CNN

To predicts impact on chromatin accessibility and histone modification

FASTA / TSV

http://deepfigv.mssm.edu

[62]