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*

*Autor correspondente para este trabalho

Resultado de pesquisarevisão de pares

3 Citações (Scopus)

Resumo

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.
Idioma originalEnglish
Páginas (de-até)1097-1105
Número de páginas9
RevistaGenome Research
Volume31
Número de emissão6
DOIs
Estado da publicaçãoPublicado - 22 abr. 2021
Publicado externamenteSim

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