A Primer on Deep Learning in Genomics

Overview

Journal Nat Genet

Specialty Genetics

Date 2018 Nov 28

PMID 30478442

Citations 297

Authors

James Zou

Mikael Huss

Abubakar Abid

Pejman Mohammadi

Ali Torkamani

Amalio Telenti

Affiliations

Soon will be listed here.

Abstract

Deep learning methods are a class of machine learning techniques capable of identifying highly complex patterns in large datasets. Here, we provide a perspective and primer on deep learning applications for genome analysis. We discuss successful applications in the fields of regulatory genomics, variant calling and pathogenicity scores. We include general guidance for how to effectively use deep learning methods as well as a practical guide to tools and resources. This primer is accompanied by an interactive online tutorial.

Citing Articles

Deep learning prioritizes cancer mutations that alter protein nucleocytoplasmic shuttling to drive tumorigenesis.

Zheng Y, Yu K, Lin J, Liang Z, Zhang Q, Li J Nat Commun. 2025; 16(1):2511.

PMID: 40087285 DOI: 10.1038/s41467-025-57858-8.

Tailored therapies for triple-negative breast cancer: current landscape and future perceptions.

Khan Y, Rizvi S, Raza A, Khan A, Hussain S, Khan N Naunyn Schmiedebergs Arch Pharmacol. 2025; .

PMID: 40029385 DOI: 10.1007/s00210-025-03896-4.

Machine Learning-Enhanced Extraction of Protein Signatures of Renal Cell Carcinoma from Proteomics Data.

Liu H, Ma Z, Lih T, Chen L, Hu Y, Wang Y bioRxiv. 2025; .

PMID: 40027663 PMC: 11870591. DOI: 10.1101/2025.02.17.638651.

A privacy-preserving dependable deep federated learning model for identifying new infections from genome sequences.

Mehedi S, Abdulrazak L, Ahmed K, Uddin M, Bui F, Chen L Sci Rep. 2025; 15(1):7291.

PMID: 40025035 PMC: 11873272. DOI: 10.1038/s41598-025-89612-x.

Sequence processing with quantum-inspired tensor networks.

Harvey C, Yeung R, Meichanetzidis K Sci Rep. 2025; 15(1):7155.

PMID: 40021695 PMC: 11871337. DOI: 10.1038/s41598-024-84295-2.

References

Shaham U, Stanton K, Zhao J, Li H, Raddassi K, Montgomery R . Removal of batch effects using distribution-matching residual networks. Bioinformatics. 2017; 33(16):2539-2546. PMC: 5870543. DOI: 10.1093/bioinformatics/btx196. View

Poplin R, Chang P, Alexander D, Schwartz S, Colthurst T, Ku A . A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018; 36(10):983-987. DOI: 10.1038/nbt.4235. View

Korvigo I, Afanasyev A, Romashchenko N, Skoblov M . Generalising better: Applying deep learning to integrate deleteriousness prediction scores for whole-exome SNV studies. PLoS One. 2018; 13(3):e0192829. PMC: 5851551. DOI: 10.1371/journal.pone.0192829. View

Sundaram L, Gao H, Padigepati S, McRae J, Li Y, Kosmicki J . Predicting the clinical impact of human mutation with deep neural networks. Nat Genet. 2018; 50(8):1161-1170. PMC: 6237276. DOI: 10.1038/s41588-018-0167-z. View

Libbrecht M, Noble W . Machine learning applications in genetics and genomics. Nat Rev Genet. 2015; 16(6):321-32. PMC: 5204302. DOI: 10.1038/nrg3920. View

Chen L, Cai C, Chen V, Lu X . Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model. BMC Bioinformatics. 2016; 17 Suppl 1:9. PMC: 4895523. DOI: 10.1186/s12859-015-0852-1. View

Tan J, Hammond J, Hogan D, Greene C . ADAGE-Based Integration of Publicly Available Gene Expression Data with Denoising Autoencoders Illuminates Microbe-Host Interactions. mSystems. 2016; 1(1). PMC: 5069748. DOI: 10.1128/mSystems.00025-15. View

. Enhancing GTEx by bridging the gaps between genotype, gene expression, and disease. Nat Genet. 2017; 49(12):1664-1670. PMC: 6636856. DOI: 10.1038/ng.3969. View

Xie R, Wen J, Quitadamo A, Cheng J, Shi X . A deep auto-encoder model for gene expression prediction. BMC Genomics. 2017; 18(Suppl 9):845. PMC: 5773895. DOI: 10.1186/s12864-017-4226-0. View

10.

Telenti A, Lippert C, Chang P, DePristo M . Deep learning of genomic variation and regulatory network data. Hum Mol Genet. 2018; 27(R1):R63-R71. PMC: 6499235. DOI: 10.1093/hmg/ddy115. View

11.

Min X, Zeng W, Chen S, Chen N, Chen T, Jiang R . Predicting enhancers with deep convolutional neural networks. BMC Bioinformatics. 2017; 18(Suppl 13):478. PMC: 5773911. DOI: 10.1186/s12859-017-1878-3. View

12.

Li Y, Shi W, Wasserman W . Genome-wide prediction of cis-regulatory regions using supervised deep learning methods. BMC Bioinformatics. 2018; 19(1):202. PMC: 5984344. DOI: 10.1186/s12859-018-2187-1. View

13.

Lin C, Jain S, Kim H, Bar-Joseph Z . Using neural networks for reducing the dimensions of single-cell RNA-Seq data. Nucleic Acids Res. 2017; 45(17):e156. PMC: 5737331. DOI: 10.1093/nar/gkx681. View

14.

Chen Y, Li Y, Narayan R, Subramanian A, Xie X . Gene expression inference with deep learning. Bioinformatics. 2016; 32(12):1832-9. PMC: 4908320. DOI: 10.1093/bioinformatics/btw074. View

15.

Luo R, Sedlazeck F, Lam T, Schatz M . A multi-task convolutional deep neural network for variant calling in single molecule sequencing. Nat Commun. 2019; 10(1):998. PMC: 6397153. DOI: 10.1038/s41467-019-09025-z. View

16.

Liu F, Li H, Ren C, Bo X, Shu W . PEDLA: predicting enhancers with a deep learning-based algorithmic framework. Sci Rep. 2016; 6:28517. PMC: 4916453. DOI: 10.1038/srep28517. View

17.

Yu N, Yu Z, Pan Y . A deep learning method for lincRNA detection using auto-encoder algorithm. BMC Bioinformatics. 2017; 18(Suppl 15):511. PMC: 5731497. DOI: 10.1186/s12859-017-1922-3. View

18.

Lanchantin J, Singh R, Wang B, Qi Y . DEEP MOTIF DASHBOARD: VISUALIZING AND UNDERSTANDING GENOMIC SEQUENCES USING DEEP NEURAL NETWORKS. Pac Symp Biocomput. 2016; 22:254-265. PMC: 5787355. DOI: 10.1142/9789813207813_0025. View

19.

LeCun Y, Bengio Y, Hinton G . Deep learning. Nature. 2015; 521(7553):436-44. DOI: 10.1038/nature14539. View

20.

Camacho D, Collins K, Powers R, Costello J, Collins J . Next-Generation Machine Learning for Biological Networks. Cell. 2018; 173(7):1581-1592. DOI: 10.1016/j.cell.2018.05.015. View