TranscriptomeBrowser: a powerful and flexible toolbox to explore productively the transcriptional landscape of the Gene Expression Omnibus database

• Published in: PloS one 2008-12, Vol.3 (12), p.e4001-e4001
• Main Authors: Lopez, Fabrice, Textoris, Julien, Bergon, Aurélie, Didier, Gilles, Remy, Elisabeth, Granjeaud, Samuel, Imbert, Jean, Nguyen, Catherine, Puthier, Denis
• Format: Article
• Language: English
• Subjects: Abnormalities; Algorithms; Analysis; Animals; Anopheles; Bioinformatics; Biologists; Biology; Boolean algebra; Breast cancer; Classification; Cluster Analysis; Clustering; Computational Biology; Computational Biology/Genomics; Computational Biology/Transcriptional Regulation; Computer Science; Cytokines; Data bases; Data processing; Databases, Genetic; DNA microarrays; Efficiency; Experiments; Gene expression; Gene Expression - physiology; Gene Expression Profiling; Gene Regulatory Networks - physiology; Genes; Genomes; Graph representations; Homeostasis; Humans; Immunoglobulins; Immunology; Kinases; Knowledge base; Markov processes; Meta-Analysis as Topic; Mice; Multiple myeloma; Oligonucleotide Array Sequence Analysis; Oncology; Operators; Quality control; Rats; Repositories; Signal Transduction - genetics; Signaling; Signatures; Software; Transcription; Transcription (Genetics); Tumors; User-Computer Interface
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0004001

As public microarray repositories are constantly growing, we are facing the challenge of designing strategies to provide productive access to the available data. We used a modified version of the Markov clustering algorithm to systematically extract clusters of co-regulated genes from hundreds of microarray datasets stored in the Gene Expression Omnibus database (n = 1,484). This approach led to the definition of 18,250 transcriptional signatures (TS) that were tested for functional enrichment using the DAVID knowledgebase. Over-representation of functional terms was found in a large proportion of these TS (84%). We developed a JAVA application, TBrowser that comes with an open plug-in architecture and whose interface implements a highly sophisticated search engine supporting several Boolean operators (http://tagc.univ-mrs.fr/tbrowser/). User can search and analyze TS containing a list of identifiers (gene symbols or AffyIDs) or associated with a set of functional terms. As proof of principle, TBrowser was used to define breast cancer cell specific genes and to detect chromosomal abnormalities in tumors. Finally, taking advantage of our large collection of transcriptional signatures, we constructed a comprehensive map that summarizes gene-gene co-regulations observed through all the experiments performed on HGU133A Affymetrix platform. We provide evidences that this map can extend our knowledge of cellular signaling pathways.