|Title||Predicting effects of noncoding variants with deep learning-based sequence model.|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Zhou, J, Troyanskaya, OG|
|Date Published||2015 Aug 24|
Identifying functional effects of noncoding variants is a major challenge in human genetics. To predict the noncoding-variant effects de novo from sequence, we developed a deep learning-based algorithmic framework, DeepSEA (http://deepsea.princeton.edu/), that directly learns a regulatory sequence code from large-scale chromatin-profiling data, enabling prediction of chromatin effects of sequence alterations with single-nucleotide sensitivity. We further used this capability to improve prioritization of functional variants including expression quantitative trait loci (eQTLs) and disease-associated variants.
|Alternate Journal||Nat. Methods|