@article{4465,
  keywords = {Humans, Regulatory Sequences, Nucleic Acid, Chromatin, Epigenomics, Quantitative Trait Loci, Human Genetics},
  author = {Kathleen Chen and Aaron Wong and Olga Troyanskaya and Jian Zhou},
  title = {A sequence-based global map of regulatory activity for deciphering human genetics.},
  abstract = { <p>Epigenomic profiling has enabled large-scale identification of regulatory elements, yet we still lack a systematic mapping from any sequence or variant to regulatory activities. We address this challenge with Sei, a framework for integrating human genetics data with sequence information to discover the regulatory basis of traits and diseases. Sei learns a vocabulary of regulatory activities, called sequence classes, using a deep learning model that predicts 21,907 chromatin profiles across >1,300 cell lines and tissues. Sequence classes provide a global classification and quantification of sequence and variant effects based on diverse regulatory activities, such as cell type-specific enhancer functions. These predictions are supported by tissue-specific expression, expression quantitative trait loci and evolutionary constraint data. Furthermore, sequence classes enable characterization of the tissue-specific, regulatory architecture of complex traits and generate mechanistic hypotheses for individual regulatory pathogenic mutations. We provide Sei as a resource to elucidate the regulatory basis of human health and disease.</p> },
  year = {2022},
  journal = {Nat Genet},
  volume = {54},
  pages = {940-949},
  month = {2022 Jul},
  issn = {1546-1718},
  doi = {10.1038/s41588-022-01102-2},
  language = {eng},
}