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

Pape, U. J., Grossmann, S., Hammer, S., Sperling, S., & Vingron, M. (2006). A new statistical model to select target sequences bound by transcription factors. Genome Informatics, 17(1), 134-140.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-8513-C Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-8514-A
Genre: Journal Article

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 Creators:
Pape, Utz J.1, Author              
Grossmann, Steffen2, Author
Hammer, Stefanie2, Author
Sperling, Silke3, Author              
Vingron, Martin4, Author              
Affiliations:
1Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433547              
2Max Planck Society, ou_persistent13              
3Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433550              
4Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              

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Free keywords: position weight matrix; binding site clusters; count statistics; number of occurrences; overlapping occurrences
 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.

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Language(s): eng - English
 Dates: 2006
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 312517
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Title: Genome Informatics
Source Genre: Journal
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Pages: - Volume / Issue: 17 (1) Sequence Number: - Start / End Page: 134 - 140 Identifier: ISSN: 0919-9454