@article{2703,
  keywords = {RNA, Untranslated, Humans, Transcription Factors, Mutation, Models, Genetic, Algorithms, Regulatory Sequences, Nucleic Acid, Polymorphism, Single Nucleotide, Chromatin, Support Vector Machine, Epigenomics, Genome, Human, Hepatocyte Nuclear Factor 3-alpha, Quantitative Trait Loci},
  author = {Jian Zhou and Olga Troyanskaya},
  title = {Predicting effects of noncoding variants with deep learning-based sequence model.},
  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> },
  year = {2015},
  journal = {Nat Methods},
  volume = {12},
  pages = {931-4},
  month = {2015 Oct},
  issn = {1548-7105},
  doi = {10.1038/nmeth.3547},
  language = {eng},
}