title: Meta-analysis of muscle transcriptome data using the MADMuscle database reveals biologically relevant gene patterns.
abstract: BACKGROUND
  DNA microarray technology has had a great impact on muscle research and microarray gene expression data has been widely used to identify gene signatures characteristic of the studied conditions. With the rapid accumulation of muscle microarray data, it is of great interest to understand how to compare and combine data across multiple studies. Meta-analysis of transcriptome data is a valuable method to achieve it. It enables to highlight conserved gene signatures between multiple independent studies. However, using it is made difficult by the diversity of the available data: different microarray platforms, different gene nomenclature, different species studied, etc.
  
  
  METHODS
  We have developed a system tool dedicated to muscle transcriptome data. This system comprises a collection of microarray data as well as a query tool. This latter allows the user to extract similar clusters of co-expressed genes from the database, using an input gene list. Common and relevant gene signatures can thus be searched more easily. The dedicated database consists in a large compendium of public data (more than 500 data sets) related to muscle (skeletal and heart). These studies included seven different animal species from invertebrates (Drosophila melanogaster, Caenorhabditis elegans) and vertebrates (Homo sapiens, Mus musculus, Rattus norvegicus, Canis familiaris, Gallus gallus). After a renormalization step, clusters of co-expressed genes were identified in each dataset. The lists of co-expressed genes were annotated using a unified re-annotation procedure. These gene lists were compared to find significant overlaps between studies.
  
  
  CONCLUSIONS
  Applied to this large compendium of data sets, meta-analyses demonstrated that conserved patterns between species could be identified. Focusing on a specific pathology (Duchenne Muscular Dystrophy) we validated results across independent studies and revealed robust biomarkers and new pathways of interest. The meta-analyses performed with MADMuscle show the usefulness of this approach. Our method can be applied to all public transcriptome data.
authors.affiliation: INSERM, U915, Nantes, F-44000 France. daniel.baron@nantes.inserm.fr
authors.forename: Daniel
authors.initials: D
authors.lastname: Baron
authors.forename: Emeric
authors.initials: E
authors.lastname: Dubois
authors.forename: Audrey
authors.initials: A
authors.lastname: Bihouée
authors.forename: Raluca
authors.initials: R
authors.lastname: Teusan
authors.forename: Marja
authors.initials: M
authors.lastname: Steenman
authors.forename: Philippe
authors.initials: P
authors.lastname: Jourdon
authors.forename: Armelle
authors.initials: A
authors.lastname: Magot
authors.forename: Yann
authors.initials: Y
authors.lastname: Péréon
authors.forename: Reiner
authors.initials: R
authors.lastname: Veitia
authors.forename: Frédérique
authors.initials: F
authors.lastname: Savagner
authors.forename: Gérard
authors.initials: G
authors.lastname: Ramstein
authors.forename: Rémi
authors.initials: R
authors.lastname: Houlgatte
mesh_terms: D000818:Animals; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D023281:Genomics; D006801:Humans; D058977:Molecular Sequence Annotation; D009132:Muscles / Q000378:metabolism*; D020388:Muscular Dystrophy, Duchenne / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software