Deep neural networks for inferring binding sites of RNA-binding proteins by using distributed representations of RNA primary sequence and secondary structure.

Journal: BMC genomics
Published Date: Dec 17, 2020

Abstract

BACKGROUND: RNA binding proteins (RBPs) play a vital role in post-transcriptional processes in all eukaryotes, such as splicing regulation, mRNA transport, and modulation of mRNA translation and decay. The identification of RBP binding sites is a crucial step in understanding the biological mechanism of post-transcriptional gene regulation. However, the determination of RBP binding sites on a large scale is a challenging task due to high cost of biochemical assays. Quite a number of studies have exploited machine learning methods to predict binding sites. Especially, deep learning is increasingly used in the bioinformatics field by virtue of its ability to learn generalized representations from DNA and protein sequences.

Authors

  • Lei Deng
    1] Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China [2] Optical Memory National Engineering Research Center, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
  • Youzhi Liu
    School of Computer Science and Engineering, Central South University, Changsha, 410075, China.
  • Yechuan Shi
    School of Computer Science and Engineering, Central South University, Changsha, 410075, China.
  • Wenhao Zhang
    Aliyun School of Big Data, Changzhou University, 213164 Changzhou, China.
  • Chun Yang
    State Key Laboratory of Biogeology and Environmental Geology, School of Earth Sciences, China University of Geosciences, Wuhan, 430074, China.
  • Hui Liu
    Institute of Urology and Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.

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