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

doi:10.1038/NMETH.3547

2015-08-24

1.5K

Jian Zhou

Olga G. Troyanskaya *

DOI：10.1038/NMETH.3547

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摘要

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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.

Keyword:

REGULATORY VARIANTS

FRAMEWORK

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期刊

Nature Methods

IF:

32.1

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被引数:

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Princeton University

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