A new statistical model to select target sequences bound by transcription factors

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Item Type:	Article
Title:	A new statistical model to select target sequences bound by transcription factors
Creators Name:	Pape, U.J., Grossmann, S., Hammer, S., Sperling, S. and Vingron, M.
Abstract:	Transcription factors (TFs) play a key role in gene regulation by binding to target sequences. In silico prediction of potential binding to a sequence is a main task in computational biology. Although many methods have been proposed to tackle this problem, the statistical significance of the prediction is still not solved. We propose an approach to give a good approximation for the potential of a sequence to be bound by a TF. Instead of assessing distinct binding sites, we motivate to focus on the number of binding sites. Based on a suitable statistical model, probabilities for scoring are approximated for a TF to bind to a sequence. Two examples show the necessity of such a model as well as the superiority of the proposed method compared to standard approaches.
Keywords:	Binding Site Clusters, Count Statistics, Number of Occurrences, Overlapping Occurrences, Position Weight Matrix, Animals, Mice
Source:	Genome Informatics
ISSN:	0919-9454
Publisher:	Universal Academy Press
Volume:	17
Number:	1
Page Range:	134-140
Date:	2006
PubMed:	View item in PubMed

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