@article{9a4a33ab11f9413da468e8b696ad4d6a,
title = "DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants",
abstract = "The complex system of gene expression is regulated by the cell type-specific binding of transcription factors (TFs) to regulatory elements. Identifying variants that disrupt TF binding and lead to human diseases remains a great challenge. To address this, we implement sequence-based deep learning models that accurately predict the TF binding intensities to given DNA sequences. In addition to accurately classifying TF-DNA binding or unbinding, our models are capable of accurately predicting real-valued TF binding intensities by leveraging large-scale TF ChIP-seq data. The changes in the TF binding intensities between the altered sequence and the reference sequence reflect the degree of functional impact for the variant. This enables us to develop the tool DeFine (Deep learning based Functional impact of non-coding variants evaluator, http://define.cbi.pku.edu.cn) with improved performance for assessing the functional impact of non-coding variants including SNPs and indels. DeFine accurately identifies the causal functional non-coding variants from disease-associated variants in GWAS. DeFine is an effective and easy-to-use tool that facilities systematic prioritization of functional non-coding variants.",
author = "Meng Wang and Cheng Tai and E. Weinan and Liping Wei",
note = "Funding Information: Development Program of China [2017YFC1201200]; Major Program of National Natural Science Foundation of China [91130005 to W.E., C.T.]; U.S. Department of Energy [DE-SC0009248 to W.E.]; U.S. Office of Naval Research [N00014-13-1-0338 to W.E.]. Funding for open access charge: National Natural Science Foundation of China [31530092]. Conflict of interest statement. None declared. Funding Information: National Natural Science Foundation of China [31530092]; Peking University Clinical Cooperation 985 Project [PKU-2013-1-06, PKU-2014-1-1]; National High-tech R&D Program (863) [2015AA020108]; National Key Research and Development Program of China [2017YFC1201200]; Major Program of National Natural Science Foundation of China [91130005 to W.E., C.T.]; U.S. Department of Energy [DE-SC0009248 to W.E.]; U.S. Office of Naval Research [N00014-13-1-0338 to W.E.]. Funding for open access charge: National Natural Science Foundation of China [31530092]. Funding Information: National Natural Science Foundation of China [31530092]; Peking University Clinical Cooperation 985 Project [PKU-2013-1-06, PKU-2014-1-1]; National High-tech R&D Program (863) [2015AA020108]; National Key Research and Publisher Copyright: {\textcopyright} The Author(s) 2018.",
year = "2018",
month = dec,
day = "1",
doi = "10.1093/NAR/GKY215",
language = "English (US)",
volume = "46",
pages = "E69",
journal = "Nucleic acids research",
issn = "0305-1048",
publisher = "Oxford University Press",
number = "11",
}