Your browser doesn't support javascript.
loading
Modeling transcriptional regulation of model species with deep learning.
Cofer, Evan M; Raimundo, João; Tadych, Alicja; Yamazaki, Yuji; Wong, Aaron K; Theesfeld, Chandra L; Levine, Michael S; Troyanskaya, Olga G.
Afiliação
  • Cofer EM; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA.
  • Raimundo J; Graduate Program in Quantitative and Computational Biology, Princeton University, Princeton, New Jersey 08544, USA.
  • Tadych A; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA.
  • Yamazaki Y; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA.
  • Wong AK; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA.
  • Theesfeld CL; Yutaka Seino Distinguished Center for Diabetes Research, Kansai Electric Power Medical Research Institute, Kobe, 650-0047, Japan.
  • Levine MS; Flatiron Institute, Simons Foundation, New York, New York 10010, USA.
  • Troyanskaya OG; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA.
Genome Res ; 31(6): 1097-1105, 2021 06.
Article em En | MEDLINE | ID: mdl-33888512
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Drosophila melanogaster / Aprendizado Profundo Tipo de estudo: Prognostic_studies Limite: Animals Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Drosophila melanogaster / Aprendizado Profundo Tipo de estudo: Prognostic_studies Limite: Animals Idioma: En Ano de publicação: 2021 Tipo de documento: Article