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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.|
|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|
|Pages:||931 - 934|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||Nature Methods|
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