PT  - JOURNAL ARTICLE
AU  - Evan M. Cofer
AU  - João Raimundo
AU  - Alicja Tadych
AU  - Yuji Yamazaki
AU  - Aaron K. Wong
AU  - Chandra L. Theesfeld
AU  - Michael S. Levine
AU  - Olga G. Troyanskaya
TI  - DeepArk: modeling <em>cis</em>-regulatory codes of model species with deep learning
AID  - 10.1101/2020.04.23.058040
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.04.23.058040
4099  - http://biorxiv.org/content/early/2020/04/28/2020.04.23.058040.short
4100  - http://biorxiv.org/content/early/2020/04/28/2020.04.23.058040.full
AB  - To enable large-scale analyses of regulatory logic in model species, we developed DeepArk (https://DeepArk.princeton.edu), a set of deep learning models of the cis-regulatory codes of 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.Competing Interest StatementThe authors have declared no competing interest.