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

Research output: Contribution to journalArticlepeer-review

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 (US)
Pages (from-to)1097-1105
Number of pages9
JournalGenome Research
Volume31
Issue number6
DOIs
StatePublished - Jun 2021

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)

Fingerprint

Dive into the research topics of 'Modeling transcriptional regulation of model species with deep learning'. Together they form a unique fingerprint.

Cite this