Predicting effects of noncoding variants with deep learning-based sequence model

Nat Methods. 2015 Oct;12(10):931-4. doi: 10.1038/nmeth.3547. Epub 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 (, 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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms*
  • Chromatin / genetics*
  • Epigenomics
  • Genome, Human
  • Hepatocyte Nuclear Factor 3-alpha / genetics
  • Humans
  • Models, Genetic
  • Mutation
  • Polymorphism, Single Nucleotide*
  • Quantitative Trait Loci*
  • RNA, Untranslated
  • Regulatory Sequences, Nucleic Acid
  • Support Vector Machine
  • Transcription Factors / genetics
  • Transcription Factors / metabolism


  • Chromatin
  • FOXA1 protein, human
  • Hepatocyte Nuclear Factor 3-alpha
  • RNA, Untranslated
  • Transcription Factors