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Identification of functional clustersof transcription factor binding motifs in genomesequences: the MSCAN algorithm

Identification of functional clustersof transcription factor binding motifs in genomesequences:... Motivation:The identification of regulatory control regions within genomesis a major challenge. Studies have demonstrated that regulatingregions can be described as locally dense clusters or modules ofcis-acting transcription factor binding sites (TFBS). Forwell-described biological contexts, it is possible to trainpredictive algorithms to discern novel modules in genomesequences. However, utility of module detection methods has beenseverely limited by insufficient training data. For only a fewtissues can one obtain sufficient numbers of literature-derivedregulatory modules. Results: We present a novel method, MSCAN, that circumvents the trainingdata problem by measuring the statistical significance of anynon-overlapping combination of TFBS in a window. Given a set oftranscription factor binding profiles, a significance threshold,and a genomic sequence, MSCAN returns putative regulatoryregions. We assess performance on two curated collections ofregulatory regions; one each for tissue-specific expression inliver and skeletal muscle cells. The efficiency of MSCAN allowsfor predictive screens of entire genomes.Availability: http://tfscan.cgb.ki.se/cgi-bin/MSCANContact: [email protected]: transcription, gene networks, modules, motif,promoter.*To whom correspondenceshould be addressed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bioinformatics Oxford University Press

Identification of functional clustersof transcription factor binding motifs in genomesequences: the MSCAN algorithm

Johansson, Ö.; Alkema, W.; Wasserman, W. W.; Lagergren, J.
Bioinformatics , Volume 19 (suppl_1): 8 – Jul 3, 2003
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References (24)

Publisher
Oxford University Press
Copyright
© Oxford University Press 2003
ISSN
1367-4803
eISSN
1460-2059
DOI
10.1093/bioinformatics/btg1021
Publisher site
See Article on Publisher Site

Abstract

Motivation:The identification of regulatory control regions within genomesis a major challenge. Studies have demonstrated that regulatingregions can be described as locally dense clusters or modules ofcis-acting transcription factor binding sites (TFBS). Forwell-described biological contexts, it is possible to trainpredictive algorithms to discern novel modules in genomesequences. However, utility of module detection methods has beenseverely limited by insufficient training data. For only a fewtissues can one obtain sufficient numbers of literature-derivedregulatory modules. Results: We present a novel method, MSCAN, that circumvents the trainingdata problem by measuring the statistical significance of anynon-overlapping combination of TFBS in a window. Given a set oftranscription factor binding profiles, a significance threshold,and a genomic sequence, MSCAN returns putative regulatoryregions. We assess performance on two curated collections ofregulatory regions; one each for tissue-specific expression inliver and skeletal muscle cells. The efficiency of MSCAN allowsfor predictive screens of entire genomes.Availability: http://tfscan.cgb.ki.se/cgi-bin/MSCANContact: [email protected]: transcription, gene networks, modules, motif,promoter.*To whom correspondenceshould be addressed.

Journal

Bioinformatics – Oxford University Press

Published: Jul 3, 2003

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