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.
- Authors:
-
- Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, 100871, P.R. China
- Center for Data Science, Peking University, Beijing, 100871, P.R. China, Beijing Institute of Big Data Research, Beijing, 100871, P.R. China
- Center for Data Science, Peking University, Beijing, 100871, P.R. China, Beijing Institute of Big Data Research, Beijing, 100871, P.R. China, Department of Mathematics and PACM, Princeton University, Princeton, NJ, 08544, USA
- Publication Date:
- Research Org.:
- Princeton Univ., NJ (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1430754
- Alternate Identifier(s):
- OSTI ID: 1502444
- Grant/Contract Number:
- SC0009248
- Resource Type:
- Published Article
- Journal Name:
- Nucleic Acids Research
- Additional Journal Information:
- Journal Name: Nucleic Acids Research Journal Volume: 46 Journal Issue: 11; Journal ID: ISSN 0305-1048
- Publisher:
- Oxford University Press
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES
Citation Formats
Wang, Meng, Tai, Cheng, E, Weinan, and Wei, Liping. DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants. United Kingdom: N. p., 2018.
Web. doi:10.1093/nar/gky215.
Wang, Meng, Tai, Cheng, E, Weinan, & Wei, Liping. DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants. United Kingdom. doi:10.1093/nar/gky215.
Wang, Meng, Tai, Cheng, E, Weinan, and Wei, Liping. Mon .
"DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants". United Kingdom. doi:10.1093/nar/gky215.
@article{osti_1430754,
title = {DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants},
author = {Wang, Meng and Tai, Cheng and E, Weinan and Wei, Liping},
abstractNote = {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.},
doi = {10.1093/nar/gky215},
journal = {Nucleic Acids Research},
number = 11,
volume = 46,
place = {United Kingdom},
year = {2018},
month = {4}
}
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DOI: 10.1093/nar/gky215
DOI: 10.1093/nar/gky215
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Cited by: 7 works
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