Simple Shared Motifs (SSM) in conserved region of promoters: a new approach to identify co-regulation patterns
<p>Abstract</p> <p>Background</p> <p>Regulation of gene expression plays a pivotal role in cellular functions. However, understanding the dynamics of transcription remains a challenging task. A host of computational approaches have been developed to identify regulatory...
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doaj-cfc5adfab8b94e7d88a46fda722278da2020-11-24T21:53:37ZengBMCBMC Bioinformatics1471-21052011-09-0112136510.1186/1471-2105-12-365Simple Shared Motifs (SSM) in conserved region of promoters: a new approach to identify co-regulation patternsThéret NathalieLeMeur NolwennLeBorgne MichelGruel Jérémy<p>Abstract</p> <p>Background</p> <p>Regulation of gene expression plays a pivotal role in cellular functions. However, understanding the dynamics of transcription remains a challenging task. A host of computational approaches have been developed to identify regulatory motifs, mainly based on the recognition of DNA sequences for transcription factor binding sites. Recent integration of additional data from genomic analyses or phylogenetic footprinting has significantly improved these methods.</p> <p>Results</p> <p>Here, we propose a different approach based on the compilation of Simple Shared Motifs (SSM), groups of sequences defined by their length and similarity and present in conserved sequences of gene promoters. We developed an original algorithm to search and count SSM in pairs of genes. An exceptional number of SSM is considered as a common regulatory pattern. The SSM approach is applied to a sample set of genes and validated using functional gene-set enrichment analyses. We demonstrate that the SSM approach selects genes that are over-represented in specific biological categories (Ontology and Pathways) and are enriched in co-expressed genes. Finally we show that genes co-expressed in the same tissue or involved in the same biological pathway have increased SSM values.</p> <p>Conclusions</p> <p>Using unbiased clustering of genes, Simple Shared Motifs analysis constitutes an original contribution to provide a clearer definition of expression networks.</p> http://www.biomedcentral.com/1471-2105/12/365 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Théret Nathalie LeMeur Nolwenn LeBorgne Michel Gruel Jérémy |
spellingShingle |
Théret Nathalie LeMeur Nolwenn LeBorgne Michel Gruel Jérémy Simple Shared Motifs (SSM) in conserved region of promoters: a new approach to identify co-regulation patterns BMC Bioinformatics |
author_facet |
Théret Nathalie LeMeur Nolwenn LeBorgne Michel Gruel Jérémy |
author_sort |
Théret Nathalie |
title |
Simple Shared Motifs (SSM) in conserved region of promoters: a new approach to identify co-regulation patterns |
title_short |
Simple Shared Motifs (SSM) in conserved region of promoters: a new approach to identify co-regulation patterns |
title_full |
Simple Shared Motifs (SSM) in conserved region of promoters: a new approach to identify co-regulation patterns |
title_fullStr |
Simple Shared Motifs (SSM) in conserved region of promoters: a new approach to identify co-regulation patterns |
title_full_unstemmed |
Simple Shared Motifs (SSM) in conserved region of promoters: a new approach to identify co-regulation patterns |
title_sort |
simple shared motifs (ssm) in conserved region of promoters: a new approach to identify co-regulation patterns |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2011-09-01 |
description |
<p>Abstract</p> <p>Background</p> <p>Regulation of gene expression plays a pivotal role in cellular functions. However, understanding the dynamics of transcription remains a challenging task. A host of computational approaches have been developed to identify regulatory motifs, mainly based on the recognition of DNA sequences for transcription factor binding sites. Recent integration of additional data from genomic analyses or phylogenetic footprinting has significantly improved these methods.</p> <p>Results</p> <p>Here, we propose a different approach based on the compilation of Simple Shared Motifs (SSM), groups of sequences defined by their length and similarity and present in conserved sequences of gene promoters. We developed an original algorithm to search and count SSM in pairs of genes. An exceptional number of SSM is considered as a common regulatory pattern. The SSM approach is applied to a sample set of genes and validated using functional gene-set enrichment analyses. We demonstrate that the SSM approach selects genes that are over-represented in specific biological categories (Ontology and Pathways) and are enriched in co-expressed genes. Finally we show that genes co-expressed in the same tissue or involved in the same biological pathway have increased SSM values.</p> <p>Conclusions</p> <p>Using unbiased clustering of genes, Simple Shared Motifs analysis constitutes an original contribution to provide a clearer definition of expression networks.</p> |
url |
http://www.biomedcentral.com/1471-2105/12/365 |
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