DeepMP: a deep learning tool to detect DNA base modifications on Nanopore sequencing data.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: DNA methylation plays a key role in a variety of biological processes. Recently, Nanopore long-read sequencing has enabled direct detection of these modifications. As a consequence, a range of computational methods have been developed to exploit Nanopore data for methylation detection. However, current approaches rely on a human-defined threshold to detect the methylation status of a genomic position and are not optimized to detect sites methylated at low frequency. Furthermore, most methods use either the Nanopore signals or the basecalling errors as the model input and do not take advantage of their combination.

Authors

  • Jose Bonet
    Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, 08028 Barcelona, Spain.
  • Mandi Chen
    Department of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden.
  • Marc Dabad
    CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08028 Barcelona, Spain.
  • Simon Heath
    CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08028 Barcelona, Spain.
  • Abel Gonzalez-Perez
    Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain. abel.gonzalez@irbbarcelona.org.
  • Nuria Lopez-Bigas
    Institute for Research in Biomedicine, Barcelona Institute of Science and Technology, Barcelona, Spain.
  • Jens Lagergren
    Department of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden.