DeeReCT-PolyA: a robust and generic deep learning method for PAS identification.

Journal: Bioinformatics (Oxford, England)
Published Date: Jul 15, 2019

Abstract

MOTIVATION: Polyadenylation is a critical step for gene expression regulation during the maturation of mRNA. An accurate and robust method for poly(A) signals (PASs) identification is not only desired for the purpose of better transcripts' end annotation, but can also help us gain a deeper insight of the underlying regulatory mechanism. Although many methods have been proposed for PAS recognition, most of them are PAS motif- and human-specific, which leads to high risks of overfitting, low generalization power, and inability to reveal the connections between the underlying mechanisms of different mammals.

Authors

  • Zhihao Xia
    Department of Computer Science and Engineering (CSE), Washington University in St Louis, St Louis, MO, USA.
  • Yu Li
    Department of Public Health, Shihezi University School of Medicine, 832000, China.
  • Bin Zhang
    Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Zhongxiao Li
    King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.
  • Yuhui Hu
    Department of Biology, Southern University of Science and Technology, Shenzhen, Guangdong Province 518055, PR China.
  • Wei Chen
    Department of Urology, Zigong Fourth People's Hospital, Sichuan, China.
  • Xin Gao
    Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA.

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