CisMiner: genome-wide in-silico cis-regulatory module prediction by fuzzy itemset mining.

Eukaryotic gene control regions are known to be spread throughout non-coding DNA sequences which may appear distant from the gene promoter. Transcription factors are proteins that coordinately bind to these regions at transcription factor binding sites to regulate gene expression. Several tools allo...

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Autores principales:	Carmen Navarro, Francisco J Lopez, Carlos Cano, Fernando Garcia-Alcalde, Armando Blanco
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c44a15a8b31c45b3b919197455045427
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spelling	oai:doaj.org-article:c44a15a8b31c45b3b9191974550454272021-11-25T05:58:39ZCisMiner: genome-wide in-silico cis-regulatory module prediction by fuzzy itemset mining.1932-620310.1371/journal.pone.0108065https://doaj.org/article/c44a15a8b31c45b3b9191974550454272014-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0108065https://doaj.org/toc/1932-6203Eukaryotic gene control regions are known to be spread throughout non-coding DNA sequences which may appear distant from the gene promoter. Transcription factors are proteins that coordinately bind to these regions at transcription factor binding sites to regulate gene expression. Several tools allow to detect significant co-occurrences of closely located binding sites (cis-regulatory modules, CRMs). However, these tools present at least one of the following limitations: 1) scope limited to promoter or conserved regions of the genome; 2) do not allow to identify combinations involving more than two motifs; 3) require prior information about target motifs. In this work we present CisMiner, a novel methodology to detect putative CRMs by means of a fuzzy itemset mining approach able to operate at genome-wide scale. CisMiner allows to perform a blind search of CRMs without any prior information about target CRMs nor limitation in the number of motifs. CisMiner tackles the combinatorial complexity of genome-wide cis-regulatory module extraction using a natural representation of motif combinations as itemsets and applying the Top-Down Fuzzy Frequent- Pattern Tree algorithm to identify significant itemsets. Fuzzy technology allows CisMiner to better handle the imprecision and noise inherent to regulatory processes. Results obtained for a set of well-known binding sites in the S. cerevisiae genome show that our method yields highly reliable predictions. Furthermore, CisMiner was also applied to putative in-silico predicted transcription factor binding sites to identify significant combinations in S. cerevisiae and D. melanogaster, proving that our approach can be further applied genome-wide to more complex genomes. CisMiner is freely accesible at: http://genome2.ugr.es/cisminer. CisMiner can be queried for the results presented in this work and can also perform a customized cis-regulatory module prediction on a query set of transcription factor binding sites provided by the user.Carmen NavarroFrancisco J LopezCarlos CanoFernando Garcia-AlcaldeArmando BlancoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 9, p e108065 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Carmen Navarro Francisco J Lopez Carlos Cano Fernando Garcia-Alcalde Armando Blanco CisMiner: genome-wide in-silico cis-regulatory module prediction by fuzzy itemset mining.
description	Eukaryotic gene control regions are known to be spread throughout non-coding DNA sequences which may appear distant from the gene promoter. Transcription factors are proteins that coordinately bind to these regions at transcription factor binding sites to regulate gene expression. Several tools allow to detect significant co-occurrences of closely located binding sites (cis-regulatory modules, CRMs). However, these tools present at least one of the following limitations: 1) scope limited to promoter or conserved regions of the genome; 2) do not allow to identify combinations involving more than two motifs; 3) require prior information about target motifs. In this work we present CisMiner, a novel methodology to detect putative CRMs by means of a fuzzy itemset mining approach able to operate at genome-wide scale. CisMiner allows to perform a blind search of CRMs without any prior information about target CRMs nor limitation in the number of motifs. CisMiner tackles the combinatorial complexity of genome-wide cis-regulatory module extraction using a natural representation of motif combinations as itemsets and applying the Top-Down Fuzzy Frequent- Pattern Tree algorithm to identify significant itemsets. Fuzzy technology allows CisMiner to better handle the imprecision and noise inherent to regulatory processes. Results obtained for a set of well-known binding sites in the S. cerevisiae genome show that our method yields highly reliable predictions. Furthermore, CisMiner was also applied to putative in-silico predicted transcription factor binding sites to identify significant combinations in S. cerevisiae and D. melanogaster, proving that our approach can be further applied genome-wide to more complex genomes. CisMiner is freely accesible at: http://genome2.ugr.es/cisminer. CisMiner can be queried for the results presented in this work and can also perform a customized cis-regulatory module prediction on a query set of transcription factor binding sites provided by the user.
format	article
author	Carmen Navarro Francisco J Lopez Carlos Cano Fernando Garcia-Alcalde Armando Blanco
author_facet	Carmen Navarro Francisco J Lopez Carlos Cano Fernando Garcia-Alcalde Armando Blanco
author_sort	Carmen Navarro
title	CisMiner: genome-wide in-silico cis-regulatory module prediction by fuzzy itemset mining.
title_short	CisMiner: genome-wide in-silico cis-regulatory module prediction by fuzzy itemset mining.
title_full	CisMiner: genome-wide in-silico cis-regulatory module prediction by fuzzy itemset mining.
title_fullStr	CisMiner: genome-wide in-silico cis-regulatory module prediction by fuzzy itemset mining.
title_full_unstemmed	CisMiner: genome-wide in-silico cis-regulatory module prediction by fuzzy itemset mining.
title_sort	cisminer: genome-wide in-silico cis-regulatory module prediction by fuzzy itemset mining.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/c44a15a8b31c45b3b919197455045427
work_keys_str_mv	AT carmennavarro cisminergenomewideinsilicocisregulatorymodulepredictionbyfuzzyitemsetmining AT franciscojlopez cisminergenomewideinsilicocisregulatorymodulepredictionbyfuzzyitemsetmining AT carloscano cisminergenomewideinsilicocisregulatorymodulepredictionbyfuzzyitemsetmining AT fernandogarciaalcalde cisminergenomewideinsilicocisregulatorymodulepredictionbyfuzzyitemsetmining AT armandoblanco cisminergenomewideinsilicocisregulatorymodulepredictionbyfuzzyitemsetmining
_version_	1718414366113529856

CisMiner: genome-wide in-silico cis-regulatory module prediction by fuzzy itemset mining.

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