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

Jian Zhou, Olga G. Troyanskaya

Research output: Contribution to journalArticle

513 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)931-934
Number of pages4
JournalNature methods
Volume12
Issue number10
DOIs
StatePublished - Sep 29 2015
Externally publishedYes

ASJC Scopus subject areas

  • Biotechnology
  • Biochemistry
  • Molecular Biology
  • Cell Biology

Fingerprint Dive into the research topics of 'Predicting effects of noncoding variants with deep learning-based sequence model'. Together they form a unique fingerprint.

  • Cite this