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Nat Methods. 2015 Oct;12(10):931-4. doi: 10.1038/nmeth.3547. Epub 2015 Aug 24.

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

Author information

  • 1Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA.
  • 2Graduate Program in Quantitative and Computational Biology, Princeton University, Princeton, New Jersey, USA.
  • 3Department of Computer Science, Princeton University, Princeton, New Jersey, USA.
  • 4Simons Center for Data Analysis, Simons Foundation, New York, New York, USA.

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.

PMID:
26301843
PMCID:
PMC4768299
DOI:
10.1038/nmeth.3547
[PubMed - indexed for MEDLINE]
Free PMC Article
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