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.