Identifying cis-regulatory sequences by word profile similarity.

<h4>Background</h4>Recognizing regulatory sequences in genomes is a continuing challenge, despite a wealth of available genomic data and a growing number of experimentally validated examples.<h4>Methodology/principal findings</h4>We discuss here a simple approach to search fo...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Garmay Leung, Michael B Eisen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7ca83058abcf458eade9fb6bd42ad697
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7ca83058abcf458eade9fb6bd42ad697
record_format	dspace
spelling	oai:doaj.org-article:7ca83058abcf458eade9fb6bd42ad6972021-11-25T06:20:33ZIdentifying cis-regulatory sequences by word profile similarity.1932-620310.1371/journal.pone.0006901https://doaj.org/article/7ca83058abcf458eade9fb6bd42ad6972009-09-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19730735/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Recognizing regulatory sequences in genomes is a continuing challenge, despite a wealth of available genomic data and a growing number of experimentally validated examples.<h4>Methodology/principal findings</h4>We discuss here a simple approach to search for regulatory sequences based on the compositional similarity of genomic regions and known cis-regulatory sequences. This method, which is not limited to searching for predefined motifs, recovers sequences known to be under similar regulatory control. The words shared by the recovered sequences often correspond to known binding sites. Furthermore, we show that although local word profile clustering is predictive for the regulatory sequences involved in blastoderm segmentation, local dissimilarity is a more universal feature of known regulatory sequences in Drosophila.<h4>Conclusions/significance</h4>Our method leverages sequence motifs within a known regulatory sequence to identify co-regulated sequences without explicitly defining binding sites. We also show that regulatory sequences can be distinguished from surrounding sequences by local sequence dissimilarity, a novel feature in identifying regulatory sequences across a genome. Source code for WPH-finder is available for download at http://rana.lbl.gov/downloads/wph.tar.gz.Garmay LeungMichael B EisenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 4, Iss 9, p e6901 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Garmay Leung Michael B Eisen Identifying cis-regulatory sequences by word profile similarity.
description	<h4>Background</h4>Recognizing regulatory sequences in genomes is a continuing challenge, despite a wealth of available genomic data and a growing number of experimentally validated examples.<h4>Methodology/principal findings</h4>We discuss here a simple approach to search for regulatory sequences based on the compositional similarity of genomic regions and known cis-regulatory sequences. This method, which is not limited to searching for predefined motifs, recovers sequences known to be under similar regulatory control. The words shared by the recovered sequences often correspond to known binding sites. Furthermore, we show that although local word profile clustering is predictive for the regulatory sequences involved in blastoderm segmentation, local dissimilarity is a more universal feature of known regulatory sequences in Drosophila.<h4>Conclusions/significance</h4>Our method leverages sequence motifs within a known regulatory sequence to identify co-regulated sequences without explicitly defining binding sites. We also show that regulatory sequences can be distinguished from surrounding sequences by local sequence dissimilarity, a novel feature in identifying regulatory sequences across a genome. Source code for WPH-finder is available for download at http://rana.lbl.gov/downloads/wph.tar.gz.
format	article
author	Garmay Leung Michael B Eisen
author_facet	Garmay Leung Michael B Eisen
author_sort	Garmay Leung
title	Identifying cis-regulatory sequences by word profile similarity.
title_short	Identifying cis-regulatory sequences by word profile similarity.
title_full	Identifying cis-regulatory sequences by word profile similarity.
title_fullStr	Identifying cis-regulatory sequences by word profile similarity.
title_full_unstemmed	Identifying cis-regulatory sequences by word profile similarity.
title_sort	identifying cis-regulatory sequences by word profile similarity.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/7ca83058abcf458eade9fb6bd42ad697
work_keys_str_mv	AT garmayleung identifyingcisregulatorysequencesbywordprofilesimilarity AT michaelbeisen identifyingcisregulatorysequencesbywordprofilesimilarity
_version_	1718413809341693952

Identifying cis-regulatory sequences by word profile similarity.

Ejemplares similares