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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