Asap: a framework for over-representation statistics for transcription factor binding sites.

<h4>Background</h4>In studies of gene regulation the efficient computational detection of over-represented transcription factor binding sites is an increasingly important aspect. Several published methods can be used for testing whether a set of hypothesised co-regulated genes share a co...

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Autores principales:	Troels T Marstrand, Jes Frellsen, Ida Moltke, Martin Thiim, Eivind Valen, Dorota Retelska, Anders Krogh
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2008
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/53693021ea754811be2918b8835263cc
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spelling	oai:doaj.org-article:53693021ea754811be2918b8835263cc2021-11-25T06:13:21ZAsap: a framework for over-representation statistics for transcription factor binding sites.1932-620310.1371/journal.pone.0001623https://doaj.org/article/53693021ea754811be2918b8835263cc2008-02-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18286180/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>In studies of gene regulation the efficient computational detection of over-represented transcription factor binding sites is an increasingly important aspect. Several published methods can be used for testing whether a set of hypothesised co-regulated genes share a common regulatory regime based on the occurrence of the modelled transcription factor binding sites. However there is little or no information available for guiding the end users choice of method. Furthermore it would be necessary to obtain several different software programs from various sources to make a well-founded choice.<h4>Methodology</h4>We introduce a software package, Asap, for fast searching with position weight matrices that include several standard methods for assessing over-representation. We have compared the ability of these methods to detect over-represented transcription factor binding sites in artificial promoter sequences. Controlling all aspects of our input data we are able to identify the optimal statistics across multiple threshold values and for sequence sets containing different distributions of transcription factor binding sites.<h4>Conclusions</h4>We show that our implementation is significantly faster than more naïve scanning algorithms when searching with many weight matrices in large sequence sets. When comparing the various statistics, we show that those based on binomial over-representation and Fisher's exact test performs almost equally good and better than the others. An online server is available at http://servers.binf.ku.dk/asap/.Troels T MarstrandJes FrellsenIda MoltkeMartin ThiimEivind ValenDorota RetelskaAnders KroghPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 3, Iss 2, p e1623 (2008)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Troels T Marstrand Jes Frellsen Ida Moltke Martin Thiim Eivind Valen Dorota Retelska Anders Krogh Asap: a framework for over-representation statistics for transcription factor binding sites.
description	<h4>Background</h4>In studies of gene regulation the efficient computational detection of over-represented transcription factor binding sites is an increasingly important aspect. Several published methods can be used for testing whether a set of hypothesised co-regulated genes share a common regulatory regime based on the occurrence of the modelled transcription factor binding sites. However there is little or no information available for guiding the end users choice of method. Furthermore it would be necessary to obtain several different software programs from various sources to make a well-founded choice.<h4>Methodology</h4>We introduce a software package, Asap, for fast searching with position weight matrices that include several standard methods for assessing over-representation. We have compared the ability of these methods to detect over-represented transcription factor binding sites in artificial promoter sequences. Controlling all aspects of our input data we are able to identify the optimal statistics across multiple threshold values and for sequence sets containing different distributions of transcription factor binding sites.<h4>Conclusions</h4>We show that our implementation is significantly faster than more naïve scanning algorithms when searching with many weight matrices in large sequence sets. When comparing the various statistics, we show that those based on binomial over-representation and Fisher's exact test performs almost equally good and better than the others. An online server is available at http://servers.binf.ku.dk/asap/.
format	article
author	Troels T Marstrand Jes Frellsen Ida Moltke Martin Thiim Eivind Valen Dorota Retelska Anders Krogh
author_facet	Troels T Marstrand Jes Frellsen Ida Moltke Martin Thiim Eivind Valen Dorota Retelska Anders Krogh
author_sort	Troels T Marstrand
title	Asap: a framework for over-representation statistics for transcription factor binding sites.
title_short	Asap: a framework for over-representation statistics for transcription factor binding sites.
title_full	Asap: a framework for over-representation statistics for transcription factor binding sites.
title_fullStr	Asap: a framework for over-representation statistics for transcription factor binding sites.
title_full_unstemmed	Asap: a framework for over-representation statistics for transcription factor binding sites.
title_sort	asap: a framework for over-representation statistics for transcription factor binding sites.
publisher	Public Library of Science (PLoS)
publishDate	2008
url	https://doaj.org/article/53693021ea754811be2918b8835263cc
work_keys_str_mv	AT troelstmarstrand asapaframeworkforoverrepresentationstatisticsfortranscriptionfactorbindingsites AT jesfrellsen asapaframeworkforoverrepresentationstatisticsfortranscriptionfactorbindingsites AT idamoltke asapaframeworkforoverrepresentationstatisticsfortranscriptionfactorbindingsites AT martinthiim asapaframeworkforoverrepresentationstatisticsfortranscriptionfactorbindingsites AT eivindvalen asapaframeworkforoverrepresentationstatisticsfortranscriptionfactorbindingsites AT dorotaretelska asapaframeworkforoverrepresentationstatisticsfortranscriptionfactorbindingsites AT anderskrogh asapaframeworkforoverrepresentationstatisticsfortranscriptionfactorbindingsites
_version_	1718413982353588224

Asap: a framework for over-representation statistics for transcription factor binding sites.

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