TITER: predicting translation initiation sites by deep learning.

Journal: Bioinformatics (Oxford, England)
PMID: 28881981

Abstract

MOTIVATION: Translation initiation is a key step in the regulation of gene expression. In addition to the annotated translation initiation sites (TISs), the translation process may also start at multiple alternative TISs (including both AUG and non-AUG codons), which makes it challenging to predict TISs and study the underlying regulatory mechanisms. Meanwhile, the advent of several high-throughput sequencing techniques for profiling initiating ribosomes at single-nucleotide resolution, e.g. GTI-seq and QTI-seq, provides abundant data for systematically studying the general principles of translation initiation and the development of computational method for TIS identification.

Authors

  • Sai Zhang
    Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.
  • Hailin Hu
    School of Medicine, Tsinghua University, Beijing 100084, China.
  • Tao Jiang
    Department of Respiratory and Critical Care Medicine, Center for Respiratory Medicine, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
  • Lei Zhang
    Division of Gastroenterology, Union Hospital, Tongji Medical College Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Jianyang Zeng
    Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China; MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China. Electronic address: zengjy321@tsinghua.edu.cn.

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