Deep-learning augmented RNA-seq analysis of transcript splicing.

Journal: Nature methods
Published Date: Apr 1, 2019

Abstract

A major limitation of RNA sequencing (RNA-seq) analysis of alternative splicing is its reliance on high sequencing coverage. We report DARTS (https://github.com/Xinglab/DARTS), a computational framework that integrates deep-learning-based predictions with empirical RNA-seq evidence to infer differential alternative splicing between biological samples. DARTS leverages public RNA-seq big data to provide a knowledge base of splicing regulation via deep learning, thereby helping researchers better characterize alternative splicing using RNA-seq datasets even with modest coverage.

Authors

  • Zijun Zhang
    Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, CA, USA.
  • Zhicheng Pan
    Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, CA, USA.
  • Yi Ying
    Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
  • Zhijie Xie
    Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
  • Samir Adhikari
    Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
  • John Phillips
    Department of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, CA, USA.
  • Russ P Carstens
    Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Douglas L Black
    Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
  • Yingnian Wu
    Department of Statistics, University of California, Los Angeles, Los Angeles, CA, USA.
  • Yi Xing
    Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, CA, USA. XINGYI@email.chop.edu.

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