Modeling transcriptional regulation of model species with deep learning

Evan M. Cofer, João Raimundo, Alicja Tadych, Yuji Yamazaki, Aaron K. Wong, Chandra L. Theesfeld, Michael S. Levine, Olga G. Troyanskaya*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

To enable large-scale analyses of transcription regulation in model species, we developed DeepArk, a set of deep learning models of the cis-regulatory activities for four widely studied species: Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, and Mus musculus. DeepArk accurately predicts the presence of thousands of different context-specific regulatory features, including chromatin states, histone marks, and transcription factors. In vivo studies show that DeepArk can predict the regulatory impact of any genomic variant (including rare or not previously observed) and enables the regulatory annotation of understudied model species.
Original languageEnglish
Pages (from-to)1097-1105
Number of pages9
JournalGenome Research
Volume31
Issue number6
DOIs
Publication statusPublished - 22 Apr 2021
Externally publishedYes

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