Enhancing the prediction of transcription factor binding sites by incorporating structural properties and nucleotide covariations.
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.
Gunewardena
Sumedha
S
Banting and Best Department of Medical Research, Donnelly CCBR, University of Toronto, Ontario, Canada. Sumedha@cantab.net
Jeavons
Peter
P
Zhang
Zhaolei
Z
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*
D003854:Deoxyribonucleotides; D014157:Transcription Factors