DeepHistone: a deep learning approach to predicting histone modifications.

Journal: BMC genomics
Published Date: Apr 4, 2019

Abstract

MOTIVATION: Quantitative detection of histone modifications has emerged in the recent years as a major means for understanding such biological processes as chromosome packaging, transcriptional activation, and DNA damage. However, high-throughput experimental techniques such as ChIP-seq are usually expensive and time-consuming, prohibiting the establishment of a histone modification landscape for hundreds of cell types across dozens of histone markers. These disadvantages have been appealing for computational methods to complement experimental approaches towards large-scale analysis of histone modifications.

Authors

  • Qijin Yin
    MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST; Department of Automation, Tsinghua University, Beijing 100084, China.
  • Mengmeng Wu
    Department of Computer Science, Tsinghua University, Beijing, China; MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, TNLIST, China. Electronic address: wmm15@mails.tsinghua.edu.cn.
  • Qiao Liu
    MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST; Department of Automation, Tsinghua University, Beijing 100084, China.
  • Hairong Lv
    Department of Automation, Tsinghua University, Beijing, China; MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, TNLIST, China. Electronic address: lvhairong@tsinghua.edu.cn.
  • Rui Jiang
    Department of Urology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.

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