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

Troels T Marstrand; Jes Frellsen; Ida Moltke; Martin Thiim; Eivind Valen; Dorota Retelska; Anders Krogh

doi:10.1371/journal.pone.0001623

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

Troels T Marstrand, Jes Frellsen, Ida Moltke, Martin Thiim, Eivind Valen, Dorota Retelska, Anders Krogh

Bioinformatik og RNA Biologi

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

35 Citationer (Scopus)

Abstract

Udgivelsesdato: 2008-null

Originalsprog	Engelsk
Tidsskrift	PLoS ONE
Vol/bind	3
Udgave nummer	2
Sider (fra-til)	e1623
ISSN	1932-6203
DOI	https://doi.org/10.1371/journal.pone.0001623
Status	Udgivet - 2008

Bibliografisk note

Keywords: Algorithms; Binding Sites; Models, Statistical; Software; Time Factors; Transcription Factors

Adgang til dokumentet

10.1371/journal.pone.0001623

Citationsformater

@article{6eec74406f5b11dd8d9f000ea68e967b,

title = "Asap: a framework for over-representation statistics for transcription factor binding sites.",

abstract = "BACKGROUND: 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. METHODOLOGY: 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. CONCLUSIONS: We show that our implementation is significantly faster than more na{\"i}ve 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/.",

author = "Marstrand, {Troels T} and Jes Frellsen and Ida Moltke and Martin Thiim and Eivind Valen and Dorota Retelska and Anders Krogh",

note = "Keywords: Algorithms; Binding Sites; Models, Statistical; Software; Time Factors; Transcription Factors",

year = "2008",

doi = "10.1371/journal.pone.0001623",

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volume = "3",

pages = "e1623",

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T1 - Asap: a framework for over-representation statistics for transcription factor binding sites.

AU - Marstrand, Troels T

AU - Frellsen, Jes

AU - Moltke, Ida

AU - Thiim, Martin

AU - Valen, Eivind

AU - Retelska, Dorota

AU - Krogh, Anders

N1 - Keywords: Algorithms; Binding Sites; Models, Statistical; Software; Time Factors; Transcription Factors

PY - 2008

Y1 - 2008

N2 - BACKGROUND: 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. METHODOLOGY: 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. CONCLUSIONS: 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/.

AB - BACKGROUND: 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. METHODOLOGY: 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. CONCLUSIONS: 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/.

U2 - 10.1371/journal.pone.0001623

DO - 10.1371/journal.pone.0001623

M3 - Journal article

C2 - 18286180

SN - 1932-6203

VL - 3

SP - e1623

JO - PLoS ONE

JF - PLoS ONE

IS - 2

ER -