SpliceRover: interpretable convolutional neural networks for improved splice site prediction.

Journal: Bioinformatics (Oxford, England)
Published Date: Dec 15, 2018

Abstract

MOTIVATION: During the last decade, improvements in high-throughput sequencing have generated a wealth of genomic data. Functionally interpreting these sequences and finding the biological signals that are hallmarks of gene function and regulation is currently mostly done using automated genome annotation platforms, which mainly rely on integrated machine learning frameworks to identify different functional sites of interest, including splice sites. Splicing is an essential step in the gene regulation process, and the correct identification of splice sites is a major cornerstone in a genome annotation system.

Authors

  • Jasper Zuallaert
    Center for Biotech Data Science, Department of Environmental Technology, Food Technology and Molecular Biotechnology, Ghent University Global Campus, Songdo, Incheon, South Korea.
  • Fréderic Godin
    IDLab, Department for Electronics and Information Systems, Ghent University, Ghent, Belgium.
  • Mijung Kim
    Center for Biotech Data Science, Department of Environmental Technology, Food Technology and Molecular Biotechnology, Ghent University Global Campus, Songdo, Incheon, South Korea.
  • Arne Soete
    Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium.
  • Yvan Saeys
    Data Mining and Modeling for Biomedicine, VIB Inflammation Research Center, Ghent, Belgium.
  • Wesley De Neve
    Center for Biotech Data Science, Department of Environmental Technology, Food Technology and Molecular Biotechnology, Ghent University Global Campus, Songdo, Incheon, South Korea.

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