Title | Predicting effects of noncoding variants with deep learning-based sequence model. |
Publication Type | Journal Article |
Year of Publication | 2015 |
Authors | Zhou, J, Troyanskaya, OG |
Journal | Nat Methods |
Volume | 12 |
Issue | 10 |
Pagination | 931-4 |
Date Published | 2015 Oct |
ISSN | 1548-7105 |
Keywords | Algorithms, Chromatin, Epigenomics, Genome, Human, Hepatocyte Nuclear Factor 3-alpha, Humans, Models, Genetic, Mutation, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Regulatory Sequences, Nucleic Acid, RNA, Untranslated, Support Vector Machine, Transcription Factors |
Abstract | <p>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. </p> |
DOI | 10.1038/nmeth.3547 |
Alternate Journal | Nat Methods |
PubMed ID | 26301843 |
PubMed Central ID | PMC4768299 |
Grant List | R01 GM071966 / GM / NIGMS NIH HHS / United States R01 HG005998 / HG / NHGRI NIH HHS / United States P50 GM071508 / GM / NIGMS NIH HHS / United States T32 HG003284 / HG / NHGRI NIH HHS / United States |