Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks

The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many noncoding variants statistically associated with human disease, nearly all such variants have unknown mechanisms. Here, we address this challenge using an approach based on a recent machine learning advance-deep convolutional neural networks (CNNs). We introduce the open source package Basset to apply CNNs to learn the functional activity of DNA sequences from genomics data. We trained Basset on a compendium of accessible genomic sites mapped in 164 cell types by DNase-seq, and demonstrate greater predictive accuracy than previous methods. Basset predictions for the change in…

• NI
  National Institutes of Health
• NI
  National Institute of Mental Health

  Awards: R01MH102416, R01MH102416-03
• NI
  National Institute of Environmental Health Sciences

  Award: ES022984-02