Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Research article

Simple Shared Motifs (SSM) in conserved region of promoters: a new approach to identify co-regulation patterns

Jérémy Gruel1*, Michel LeBorgne2, Nolwenn LeMeur1 and Nathalie Théret1

Author Affiliations

1 EA 4427 SeRAIC IFR140, Université de Rennes 1, 2 avenue du Pr. Léon Bernard, Rennes, 35043, France

2 IRISA, Université de Rennes 1, 263 avenue du Général Leclerc, Rennes, 35042, France

For all author emails, please log on.

BMC Bioinformatics 2011, 12:365  doi:10.1186/1471-2105-12-365

Published: 12 September 2011

Abstract

Background

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.

Results

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.

Conclusions

Using unbiased clustering of genes, Simple Shared Motifs analysis constitutes an original contribution to provide a clearer definition of expression networks.