RT Journal
A1 Cofer, Evan M
A1 Raimundo, João
A1 Tadych, Alicja
A1 Yamazaki, Yuji
A1 Wong, Aaron K
A1 Theesfeld, Chandra L
A1 Levine, Michael S
A1 Troyanskaya, Olga G
T1 Modeling transcriptional regulation of model species with deep learning
JF Genome Research 
JO Genome Research 
YR 2021 
FD April 22 
DO 10.1101/gr.266171.120 
SP gr.266171.120 
UL http://genome.cshlp.org/content/early/2021/04/22/gr.266171.120.abstract 
AB To enable large-scale analyses of regulatory logic in model species, we developed DeepArk, 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.