title: Predicting effects of noncoding variants with deep learning-based sequence model.
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.
authors: Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA.|Graduate Program in Quantitative and Computational Biology, Princeton University, Princeton, New Jersey, USA.
authors: Jian
authors: J
authors: Zhou
authors: Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA.|Department of Computer Science, Princeton University, Princeton, New Jersey, USA.|Simons Center for Data Analysis, Simons Foundation, New York, New York, USA.
authors: Olga G
authors: OG
authors: Troyanskaya
mesh_terms: D000465:Algorithms; D002843:Chromatin / Q000235:genetics*; D057890:Epigenomics; D015894:Genome, Human; D051541:Hepatocyte Nuclear Factor 3-alpha / Q000235:genetics; D006801:Humans; D008957:Models, Genetic; D009154:Mutation; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci; D022661:RNA, Untranslated; D012045:Regulatory Sequences, Nucleic Acid; D060388:Support Vector Machine; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
chemical_list: D002843:Chromatin; C496499:FOXA1 protein, human; D051541:Hepatocyte Nuclear Factor 3-alpha; D022661:RNA, Untranslated; D014157:Transcription Factors