Improved predictions of transcription factor binding sites using physicochemical features of DNA

Maienschein-Cline, Mark; Dinner, Aaron R.; Hlavacek, William S.; Mu, Fangping

doi:10.1093/nar/gks771

Title: Improved predictions of transcription factor binding sites using physicochemical features of DNA

Journal Article · Fri Aug 24 00:00:00 EDT 2012 · Nucleic Acids Research

DOI:https://doi.org/10.1093/nar/gks771· OSTI ID:1625507

Maienschein-Cline, Mark ^[1]; Dinner, Aaron R. ^[1]; Hlavacek, William S. ^[2]; Mu, Fangping ^[2]

Univ. of Chicago, IL (United States). Dept. of Chemistry
Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Theoretical Biology and Biophysics Group; Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Biology

Typical approaches for predicting transcription factor binding sites (TFBSs) involve use of a position-specific weight matrix (PWM) to statistically characterize the sequences of the known sites. Recently, an alternative physicochemical approach, called SiteSleuth, was proposed. In this approach, a linear support vector machine (SVM) classifier is trained to distinguish TFBSs from background sequences based on local chemical and structural features of DNA. SiteSleuth appears to generally perform better than PWM-based methods. Here, we improve the SiteSleuth approach by considering both new physicochemical features and algorithmic modifications. New features are derived from Gibbs energies of amino acid–DNA interactions and hydroxyl radical cleavage profiles of DNA. Algorithmic modifications consist of inclusion of a feature selection step, use of a nonlinear kernel in the SVM classifier, and use of a consensus-based post-processing step for predictions. We also considered SVM classification based on letter features alone to distinguish performance gains from use of SVM-based models versus use of physicochemical features. The accuracy of each of the variant methods considered was assessed by cross validation using data available in the RegulonDB database for 54 Escherichia coli TFs, as well as by experimental validation using published ChIP-chip data available for Fis and Lrp.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division; National Institutes of Health (NIH)

Grant/Contract Number:: AC52-06NA25396; RR018754; GM085273; GM081892

OSTI ID:: 1625507

Journal Information:: Nucleic Acids Research, Vol. 40, Issue 22; ISSN 0305-1048

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United States

Language:: English

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