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

Author(s): Zhou, J; Troyanskaya, Olga G

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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.
Publication Date: 24-Aug-2015
Electronic Publication Date: 24-Aug-2015
Citation: Zhou, J, Troyanskaya, OG. (2015). Predicting effects of noncoding variants with deep learning-based sequence model. Nature Methods, 12 (931 - 934. doi:10.1038/nmeth.3547
DOI: doi:10.1038/nmeth.3547
Pages: 931 - 934
Type of Material: Journal Article
Journal/Proceeding Title: Nature Methods
Version: Author's manuscript



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