TY - JOUR
T1 - Modeling transcriptional regulation of model species with deep learning
AU - Cofer, Evan M.
AU - Raimundo, João
AU - Tadych, Alicja
AU - Yamazaki, Yuji
AU - Wong, Aaron K.
AU - Theesfeld, Chandra L.
AU - Levine, Michael S.
AU - Troyanskaya, Olga G.
N1 - Publisher Copyright:
© 2021 Cofer et al.
PY - 2021/4/22
Y1 - 2021/4/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85107711423&partnerID=8YFLogxK
U2 - 10.1101/gr.266171.120
DO - 10.1101/gr.266171.120
M3 - Article
C2 - 33888512
SN - 1088-9051
VL - 31
SP - 1097
EP - 1105
JO - Genome Research
JF - Genome Research
IS - 6
ER -