Using Sequence-Specific Chemical and Structural Properties of DNA to Predict Transcription Factor Binding Sites
Abstract
An important step in understanding gene regulation is to identify the DNA binding sites recognized by each transcription factor (TF). Conventional approaches to prediction of TF binding sites involve the definition of consensus sequences or position-specific weight matrices and rely on statistical analysis of DNA sequences of known binding sites. Here, we present a method called SiteSleuth in which DNA structure prediction, computational chemistry, and machine learning are applied to develop models for TF binding sites. In this approach, binary classifiers are trained to discriminate between true and false binding sites based on the sequence-specific chemical and structural features of DNA. These features are determined via molecular dynamics calculations in which we consider each base in different local neighborhoods. For each of 54 TFs in Escherichia coli, for which at least five DNA binding sites are documented in RegulonDB, the TF binding sites and portions of the non-coding genome sequence are mapped to feature vectors and used in training. According to cross-validation analysis and a comparison of computational predictions against ChIP-chip data available for the TF Fis, SiteSleuth outperforms three conventional approaches: Match, MATRIX SEARCH, and the method of Berg and von Hippel. SiteSleuth also outperforms QPMEME, a method similarmore »
- Authors:
-
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Theoretical Division. Theoretical Biology and Biophysics Group
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Bioscience Division. National Stable Isotope Resource
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
- OSTI Identifier:
- 1627207
- Grant/Contract Number:
- AC52-06NA25396
- Resource Type:
- Accepted Manuscript
- Journal Name:
- PLoS Computational Biology (Online)
- Additional Journal Information:
- Journal Name: PLoS Computational Biology (Online); Journal Volume: 6; Journal Issue: 11; Journal ID: ISSN 1553-7358
- Publisher:
- Public Library of Science
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; Biochemistry & Molecular Biology; Mathematical & Computational Biology
Citation Formats
Bauer, Amy L., Hlavacek, William S., Unkefer, Pat J., and Mu, Fangping. Using Sequence-Specific Chemical and Structural Properties of DNA to Predict Transcription Factor Binding Sites. United States: N. p., 2010.
Web. doi:10.1371/journal.pcbi.1001007.
Bauer, Amy L., Hlavacek, William S., Unkefer, Pat J., & Mu, Fangping. Using Sequence-Specific Chemical and Structural Properties of DNA to Predict Transcription Factor Binding Sites. United States. https://doi.org/10.1371/journal.pcbi.1001007
Bauer, Amy L., Hlavacek, William S., Unkefer, Pat J., and Mu, Fangping. Thu .
"Using Sequence-Specific Chemical and Structural Properties of DNA to Predict Transcription Factor Binding Sites". United States. https://doi.org/10.1371/journal.pcbi.1001007. https://www.osti.gov/servlets/purl/1627207.
@article{osti_1627207,
title = {Using Sequence-Specific Chemical and Structural Properties of DNA to Predict Transcription Factor Binding Sites},
author = {Bauer, Amy L. and Hlavacek, William S. and Unkefer, Pat J. and Mu, Fangping},
abstractNote = {An important step in understanding gene regulation is to identify the DNA binding sites recognized by each transcription factor (TF). Conventional approaches to prediction of TF binding sites involve the definition of consensus sequences or position-specific weight matrices and rely on statistical analysis of DNA sequences of known binding sites. Here, we present a method called SiteSleuth in which DNA structure prediction, computational chemistry, and machine learning are applied to develop models for TF binding sites. In this approach, binary classifiers are trained to discriminate between true and false binding sites based on the sequence-specific chemical and structural features of DNA. These features are determined via molecular dynamics calculations in which we consider each base in different local neighborhoods. For each of 54 TFs in Escherichia coli, for which at least five DNA binding sites are documented in RegulonDB, the TF binding sites and portions of the non-coding genome sequence are mapped to feature vectors and used in training. According to cross-validation analysis and a comparison of computational predictions against ChIP-chip data available for the TF Fis, SiteSleuth outperforms three conventional approaches: Match, MATRIX SEARCH, and the method of Berg and von Hippel. SiteSleuth also outperforms QPMEME, a method similar to SiteSleuth in that it involves a learning algorithm. The main advantage of SiteSleuth is a lower false positive rate.},
doi = {10.1371/journal.pcbi.1001007},
journal = {PLoS Computational Biology (Online)},
number = 11,
volume = 6,
place = {United States},
year = {Thu Nov 18 00:00:00 EST 2010},
month = {Thu Nov 18 00:00:00 EST 2010}
}
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