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

Research output: Contribution to journalArticle

523 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

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Biochemistry
  • Molecular Biology
  • Cell Biology

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