ArticleNucleic acids research2012
Improved predictions of transcription factor binding sites using physicochemical features of DNA.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 35 citations in OpenAlex.
- A deterministic code for transcription factor-DNA recognition through computation of binding interfaces.NAR genomics and bioinformatics · 2022Article
- Transversions have larger regulatory effects than transitions.BMC genomics · 2017Article
- Unveiling DNA structural features of promoters associated with various types of TSSs in prokaryotic transcriptomes and their role in gene expression.DNA research : an international journal for rapid publication of reports on genes and genomes · 2017Article
- Quantitative modeling of gene expression using DNA shape features of binding sites.Nucleic acids research · 2016Article
- Knowledge-based three-body potential for transcription factor binding site prediction.IET systems biology · 2016Article
- Decoding the non-coding genome: elucidating genetic risk outside the coding genome.Genes, brain, and behavior · 2016Review
- An affinity-structure database of helix-turn-helix: DNA complexes with a universal coordinate system.BMC bioinformatics · 2015Article
- Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast.PLoS computational biology · 2015Article
- Article
- The pattern of DNA cleavage intensity around indels.Scientific reports · 2015Article
- GBshape: a genome browser database for DNA shape annotations.Nucleic acids research · 2015Article
- Genome-wide analysis of transcription factor binding sites and their characteristic DNA structures.BMC genomics · 2015Article
- Absence of a simple code: how transcription factors read the genome.Trends in biochemical sciences · 2014Review
- TFBSshape: a motif database for DNA shape features of transcription factor binding sites.Nucleic acids research · 2014Article
- CollecTF: a database of experimentally validated transcription factor-binding sites in Bacteria.Nucleic acids research · 2014Article
- Binding of nucleoid-associated protein fis to DNA is regulated by DNA breathing dynamics.PLoS computational biology · 2013Article
- Article
- Functional implications of local DNA structures in regulatory motifs.TheScientificWorldJournal · 2013Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.