A primer on deep learning in genomics.

Journal: Nature genetics
Published Date:

Abstract

Deep learning methods are a class of machine learning techniques capable of identifying highly complex patterns in large datasets. Here, we provide a perspective and primer on deep learning applications for genome analysis. We discuss successful applications in the fields of regulatory genomics, variant calling and pathogenicity scores. We include general guidance for how to effectively use deep learning methods as well as a practical guide to tools and resources. This primer is accompanied by an interactive online tutorial.

Authors

  • James Zou
    Department of Biomedical Data Science, Stanford University, Stanford, California.
  • Mikael Huss
    Peltarion, Stockholm, Sweden.
  • Abubakar Abid
    Hugging Face, New York, NY, USA.
  • Pejman Mohammadi
    Scripps Research Translational Institute, La Jolla, CA, USA.
  • Ali Torkamani
    Scripps Research Translational Institute, La Jolla, CA, USA.
  • Amalio Telenti
    J. Craig Venter InstituteLa Jolla, CAUnited States.