Predicting effects of noncoding variants with deep learning-based sequence model
Author(s): Zhou, J; Troyanskaya, Olga G.
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Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhou, J | - |
dc.contributor.author | Troyanskaya, Olga G. | - |
dc.date.accessioned | 2018-07-20T15:09:30Z | - |
dc.date.available | 2018-07-20T15:09:30Z | - |
dc.date.issued | 2015-08-24 | en_US |
dc.identifier.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 | en_US |
dc.identifier.uri | http://arks.princeton.edu/ark:/88435/pr1t09q | - |
dc.description.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. | en_US |
dc.format.extent | 931 - 934 | en_US |
dc.language.iso | en_US | en_US |
dc.relation.ispartof | Nature Methods | en_US |
dc.rights | Author's manuscript | en_US |
dc.title | Predicting effects of noncoding variants with deep learning-based sequence model | en_US |
dc.type | Journal Article | en_US |
dc.identifier.doi | doi:10.1038/nmeth.3547 | - |
dc.date.eissued | 2015-08-24 | en_US |
pu.type.symplectic | http://www.symplectic.co.uk/publications/atom-terms/1.0/journal-article | en_US |
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