Enhancing the Prediction of Transcription Factor Binding Sites by Incorporating Structural Properties and Nucleotide Covariations
To cite this article:
Sumedha Gunewardena, Peter Jeavons, Zhaolei Zhang.
Journal of Computational Biology.
May 2006,
13(4): 929-945.
doi:10.1089/cmb.2006.13.929.
Banting and Best Department of Medical Research, Donnelly CCBR, University of Toronto, 160 College St., Toronto, Ontario, M5S 3E1, Canada.
Peter Jeavons
Oxford University Computing Laboratory, Parks Road, Oxford, OX1 3QD, UK.
Zhaolei Zhang
Banting and Best Department of Medical Research, Donnelly CCBR, University of Toronto, 160 College St., Toronto, Ontario, M5S 3E1, Canada.
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.
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