Table 2.

Transcriptomic-level deep-learning applications

Method name	Year	Main functionalities	Datasets	Model	Species	Tissue/cell types
Promoter/TSS:
CNNProm69	2017	promoter recognition	• EPD70 o human omouse o Arabidopsis • RegulonDB71 o 839 E. coli promoters • DBTBS72 o 746 B. subtilis promoters	•1–2 layer CNN (250 bp for eukaryotes and 85 bp input for prokaryotes)	human mouse Arabidopsis E. coli B. subtilis	Non-specific
DeeReCT-PromID73	2019	promoter recognition on highly imbalanced dataset	•16,455 human promoter sequences from EPD	•two-branch CNN, one branch with pooling layer, the other without pooling (600 bp input) •Iteratively enriching hard examples during training	human	non-specific
DeeReCT-TSS74	2021	promoter recognition guided by RNA-seq	• FANTOM539 o RNA-seq from 10 cell lines o CAGE-seq identified TSS from 10 cell lines	• two-branch CNN (1,001 bp input) o one for sequence and one RNA-seq base coverage	human	multiple (10 cell types)
Splicing:
Barash et al.75	2010	splicing prediction of cassette exons	•Microarray profile of 3,665 cassette exons in 27 mouse tissues from Fagnani et al.76 •1,014-dim features extracted from flanking sequence of the cassette exon	•a dedicated probabilistic model to estimate ($q_{inc},q_{exc},q_{nc})$ from microarray profiles •a one-layer NN (1,014 dim input) to predict the above probabilities from features of flanking sequence	mouse	multiple (27 tissues)
Leung et al.77	2014	splicing prediction of cassette exons	•RNA-seq profile of 11,019 cassette exons in five mouse tissues from Brawand et al.78 •1,393-dim features extracted from flanking sequence of the cassette exon	•MLP (1,393 dim input), with indices of two tissues to compare	mouse	multiple (5 tissues)
Xiong et al.79	2015	splicing prediction of cassette exons in exon triplets	• Bodymap 2.0 (NCBI accession GEO: GSE30611) o 10,689 cassette exons o 16 normal tissues •1,393-dim features extracted from flanking sequence of the cassette exon	•MLP (1,393 dim input) •use Bayesian MCMC for learning without overfitting	human	multiple (16 tissues)
DARTS80	2019	splicing prediction of cassette exons guided by RNA-seq	•training: ENCODE8 •K562 and HepG2 shRNA RBP knockdown datasets •testing: Roadmap Epigenomics31 RNA-seq data	• MLP (2,926 + 1,498 × 2 dim input) o 2,926 cis sequence features o 1,498 × 2 RBP expression levels o BHT integration and deep-learning prediction, and RNA-seq evidence	human	K562, HepG2
SpliceAI81	2019	Splice sites prediction from pre-mRNA	• GENCODE82 v24 isoforms o train: 13,384 genes, 130,796 donor-acceptor pairs o test: 1,652 genes, 14,289 donor-acceptor pairs • GTEx10 (novel isoforms) o 67,012 splice donors, 62,911 splice acceptors	• CNN with dilated convolution62 and residual block (5,000 nt input) • dense classification of (no splice site, donor, acceptor)	human	non-specific
Pangolin83	2022	splice sites prediction from pre-mRNA	• reference transcripts o GENCODE v34 for human transcripts o ENSEMBL84 release 100 for rhesus monkey transcripts o GENCODE m25 for mouse transcripts o ENSEMBL release 101 for rat transcripts •RNA-seq data of the four tissues (heart, liver, brain, and testis) of human, rhesus monkey, mouse, and rat from Cardoso-Moreira et al.85	•CNN with dilated convolution and residual blocks (15,000 nt input) •predicts per-tissue splicing event	human rhesus monkey mouse rat	multiple (4 tissues in each species)
Polyadenylation:
Leung et al., 201886	2018	PAS quantification (pairwise comparison)	• dataset for PAS reference o PolyA_DB 287 o GENCODE o APADB88 o Derti et al. (polyA-seq data)89 o Lianoglou et al.90 (3′-seq data) •dataset for PAS quantification •Lianoglou et al.90(3′-seq data)	•two-branch CNN for PAS pairwise comparison	human	multiple (7 tissue types)
DeeReCT-PolyA91	2019	PAS recognition	• Dragon human poly(A) dataset92 o 14,740 sequences for the 12 main human PAS motif variants • Omni human poly(A) dataset93 o 18,786 positive true PAS sequences for 12 human PAS motif variants •Xiao et al.94 3′-READS sequencing of mouse fibroblast cells of C57BL/6J (BL), SPRET/EiJ (SP), and their F1	•CNN with group normalization95 (200 nt input)	human mouse	non-specific
APARENT96	2019	PAS quantification	• 3 million APA massively parallel reporter assay from 13 libraries o use 9 out of 13 libraries for training, ∼2.4 million variants; the other four are held out entirely	•two-layer CNN (186 nt, a length that all randomized regions of the reporters can fit in) •prediction of the inclusion ratio of the proximal PAS in the reporter assay •gradient-based forward engineering of PAS sequences	human	non-specific
DeeReCT-APA97	2021	PAS quantification	•Xiao et al.94 3′-READS sequencing of mouse fibroblast cells of C57BL/6J (BL), SPRET/EiJ (SP), and their F1	• CNN + BiLSTM (448 nt per each PAS, variable PASs in each example) • models the interactions between competing PAS	mouse (BL, SP, and BLxSP F1 hybrid)	fibroblast
RNA subcellular localization:
RNATracker98	2019	subcellular localization prediction	•mRNA sequences from Ensembl84 2017 release •RNA secondary structure implied from RNAplfold99 • mRNA subcellular localization profiles o CeFra-Seq data from Benoit Bouvrette et al., 2018100 • cytosol, nuclear, membranes, insoluble o APEX-RIP data from Kaewsapsak et al.101 • ER, mitochondrial, cytosol, nuclear	•CNN + BiLSTM (∼200 nt to more than 30,000 nt)	human	HepG2 and HEK293T cell lines
MicroRNA targets:
MiRTDL102	2015	microRNA targets prediction	• TarBase dataset103 o 1,297 positive miRNA + mRNA pairs and 309 negative pairs (human, mouse, and rat) o Dataset further extended by a constraint relaxing method, 198,620 positive pairs, and 19,660 negative pairs	•CNN prediction based on 20 features of the miRNA and mRNA pair	human mouse rat	non-specific
Translation:
Cuperus et al.104	2017	5′ UTR translational efficiency prediction	•measurement of 489,348 50-nt-long 5′ UTR of yeast in a massively parallel growth selection experiment	•3-layer CNN (>50 nt to fit the randomized region) •forward engineering of 5′ UTR sequences	yeast	N/A
RNA-protein binding:
DeepBind17	2015	sequence-based RNA-protein binding prediction	• RNAcompete105 o 207 distinct RBPs from 24 eukaryotes	•CNN (101 nt input)	multiple (24 eukaryotes)	non-specific
NucleicNet106	2019	structure-based RNA-protein binding prediction	•483 RNA-protein complexes from PDB107 and de-duplicated to 158 ribonucleoprotein structures	•CNN with ResNet-like architecture	multiple	non-specific