TY  - JOUR
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
Y1  - 2021/04/22 
JF  - Genome Research 
JO  - Genome Research 
DO  - 10.1101/gr.266171.120 
SP  - gr.266171.120 
UR  - http://genome.cshlp.org/content/early/2021/04/22/gr.266171.120.abstract 
N2  - 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. 
ER  -