Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks
- 1Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts 02138, USA;
- 2School of Engineering and Applied Science, Harvard University, Cambridge, Massachusetts 02138, USA
- Corresponding author:dkelley{at}fas.harvard.edu
Abstract
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 accessibility between variant alleles were far greater for Genome-wide association study (GWAS) SNPs that are likely to be causal relative to nearby SNPs in linkage disequilibrium with them. With Basset, a researcher can perform a single sequencing assay in their cell type of interest and simultaneously learn that cell's chromatin accessibility code and annotate every mutation in the genome with its influence on present accessibility and latent potential for accessibility. Thus, Basset offers a powerful computational approach to annotate and interpret the noncoding genome.
Footnotes
[Supplemental material is available for this article.]
Article published online before print. Article, supplemental material, and publication date are athttp://www.genome.org/cgi/doi/10.1101/gr.200535.115.
Freely available online through theGenome Research Open Access option.
- ReceivedOctober 4, 2015.
- AcceptedApril 26, 2016.
This article, published inGenome Research, is available under a Creative Commons License (Attribution 4.0 International), as described athttp://creativecommons.org/licenses/by/4.0/.
