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Supervised enhancer prediction with epigenetic pattern recognition and targeted validation

Authors
  • Sethi, Anurag1
  • Gu, Mengting2, 1
  • Gumusgoz, Emrah3
  • Chan, Landon4
  • Yan, Koon-Kiu1
  • Rozowsky, Joel1
  • Barozzi, Iros5
  • Afzal, Veena5
  • Akiyama, Jennifer A.5
  • Plajzer-Frick, Ingrid5
  • Yan, Chengfei1
  • Novak, Catherine S.5
  • Kato, Momoe5
  • Garvin, Tyler H.5
  • Pham, Quan5
  • Harrington, Anne5
  • Mannion, Brandon J.5
  • Lee, Elizabeth A.5
  • Fukuda-Yuzawa, Yoko5
  • Visel, Axel5
  • And 5 more
  • 1 Yale University, New Haven, CT, USA , New Haven (United States)
  • 2 Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA , New Haven (United States)
  • 3 Section of Infectious Diseases, Yale University School of Medicine, New Haven, CT, USA , New Haven (United States)
  • 4 The Chinese University of Hong Kong, Hong Kong, China , Hong Kong (China)
  • 5 Functional Genomics Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA , Berkeley (United States)
Type
Published Article
Journal
Nature Methods
Publisher
Springer Nature
Publication Date
Jul 29, 2020
Volume
17
Issue
8
Pages
807–814
Identifiers
DOI: 10.1038/s41592-020-0907-8
Source
Springer Nature
License
Yellow

Abstract

Supervised machine-learning models trained using Drosophila epigenetic and STARR-seq data can be transferred to predict mouse and human enhancers.

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