title: Enhancing the prediction of transcription factor binding sites by incorporating structural properties and nucleotide covariations.
abstract: A problem faced by many algorithms for finding transcription factor (TF) binding sites is the high number of false positive hits that result with the increased sensitivity of their prediction. A main contributing factor to this is the short and degenerate nature of these sites which results in a low signal-to-noise ratio. In order to counter this problem, one needs to look beyond the assumption that individual bases of a TF binding site act independently from each other when binding to a transcription factor. In this paper, we present a new method based on templates, designed to exploit the discriminatory features, nucleotide polymorphism, and structural homology present in TF binding sites for distinguishing them from nonbinding sites.
authors.affiliation: Banting and Best Department of Medical Research, Donnelly CCBR, University of Toronto, Ontario, Canada. Sumedha@cantab.net
authors.forename: Sumedha
authors.initials: S
authors.lastname: Gunewardena
authors.forename: Peter
authors.initials: P
authors.lastname: Jeavons
authors.forename: Zhaolei
authors.initials: Z
authors.lastname: Zhang
mesh_terms: D001665:Binding Sites; D019295:Computational Biology / Q000379:methods; D003854:Deoxyribonucleotides / Q000737:chemistry* / Q000378:metabolism; D004926:Escherichia coli / Q000235:genetics; D011237:Predictive Value of Tests; D017422:Sequence Analysis, DNA; D013698:Templates, Genetic; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D003854:Deoxyribonucleotides; D014157:Transcription Factors