Genomics enters the deep learning era

• Published in: PeerJ (San Francisco, CA) Vol. 10; p. e13613
• Main Authors: Routhier, Etienne, Mozziconacci, Julien
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 24.06.2022; PeerJ, Inc; PeerJ; PeerJ Inc
• Subjects: Annotations; Binding sites; Bioinformatics; Computational Biology; Computational linguistics; Computational Science; Deep Learning; DNA; DNA sequencing; Epigenetic inheritance; Epigenetics; Epigenomics; Gene expression; Genetics; Genomes; Genomics; Language processing; Life Sciences; Machine learning; Machine vision; Medical Genetics; Natural language interfaces; Neural networks; Neural Networks, Computer; Nucleotide sequence; Nucleotide sequencing; Protein binding; Review; Synthetic Biology; Deep learning; Metagenomics; Epigenomics; Neural networks; Genomics; Synthetic genomes; Personalized medecine; Genetics; Review; Bioinformatics; Subjects Bioinformatics Genomics Synthetic Biology Medical Genetics Computational Science Deep learning Genomics Genetics Epigenomics Bioinformatics Review Neural networks Personalized medecine Synthetic genomes Metagenomics; Medical Genetics; Subjects Bioinformatics; Synthetic Biology; Computational Science Deep learning
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.13613

The tremendous amount of biological sequence data available, combined with the recent methodological breakthrough in deep learning in domains such as computer vision or natural language processing, is leading today to the transformation of bioinformatics through the emergence of deep genomics, the application of deep learning to genomic sequences. We review here the new applications that the use of deep learning enables in the field, focusing on three aspects: the functional annotation of genomes, the sequence determinants of the genome functions and the possibility to write synthetic genomic sequences.