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Measuring similarities between transcription factor binding sites

BACKGROUND: Collections of transcription factor binding profiles (Transfac, Jaspar) are essential to identify regulatory elements in DNA sequences. Subsets of highly similar profiles complicate large scale analysis of transcription factor binding sites. RESULTS: We propose to identify and group simi...

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Detalles Bibliográficos
Autores principales: Kielbasa, Szymon M, Gonze, Didier, Herzel, Hanspeter
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1261160/
https://www.ncbi.nlm.nih.gov/pubmed/16191190
http://dx.doi.org/10.1186/1471-2105-6-237
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author Kielbasa, Szymon M
Gonze, Didier
Herzel, Hanspeter
author_facet Kielbasa, Szymon M
Gonze, Didier
Herzel, Hanspeter
author_sort Kielbasa, Szymon M
collection PubMed
description BACKGROUND: Collections of transcription factor binding profiles (Transfac, Jaspar) are essential to identify regulatory elements in DNA sequences. Subsets of highly similar profiles complicate large scale analysis of transcription factor binding sites. RESULTS: We propose to identify and group similar profiles using two independent similarity measures: χ(2 )distances between position frequency matrices (PFMs) and correlation coefficients between position weight matrices (PWMs) scores. CONCLUSION: We show that these measures complement each other and allow to associate Jaspar and Transfac matrices. Clusters of highly similar matrices are identified and can be used to optimise the search for regulatory elements. Moreover, the application of the measures is illustrated by assigning E-box matrices of a SELEX experiment and of experimentally characterised binding sites of circadian clock genes to the Myc-Max cluster.
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spelling pubmed-12611602005-10-27 Measuring similarities between transcription factor binding sites Kielbasa, Szymon M Gonze, Didier Herzel, Hanspeter BMC Bioinformatics Software BACKGROUND: Collections of transcription factor binding profiles (Transfac, Jaspar) are essential to identify regulatory elements in DNA sequences. Subsets of highly similar profiles complicate large scale analysis of transcription factor binding sites. RESULTS: We propose to identify and group similar profiles using two independent similarity measures: χ(2 )distances between position frequency matrices (PFMs) and correlation coefficients between position weight matrices (PWMs) scores. CONCLUSION: We show that these measures complement each other and allow to associate Jaspar and Transfac matrices. Clusters of highly similar matrices are identified and can be used to optimise the search for regulatory elements. Moreover, the application of the measures is illustrated by assigning E-box matrices of a SELEX experiment and of experimentally characterised binding sites of circadian clock genes to the Myc-Max cluster. BioMed Central 2005-09-28 /pmc/articles/PMC1261160/ /pubmed/16191190 http://dx.doi.org/10.1186/1471-2105-6-237 Text en Copyright © 2005 Kielbasa et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software
Kielbasa, Szymon M
Gonze, Didier
Herzel, Hanspeter
Measuring similarities between transcription factor binding sites
title Measuring similarities between transcription factor binding sites
title_full Measuring similarities between transcription factor binding sites
title_fullStr Measuring similarities between transcription factor binding sites
title_full_unstemmed Measuring similarities between transcription factor binding sites
title_short Measuring similarities between transcription factor binding sites
title_sort measuring similarities between transcription factor binding sites
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1261160/
https://www.ncbi.nlm.nih.gov/pubmed/16191190
http://dx.doi.org/10.1186/1471-2105-6-237
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