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Bioinformatics for Transcriptome Data Mining

by evgnadmin — last modified 2008-02-04 15:05

The aim of this platform is to provide EVGN scientists with a comprehensive set of data mining tools for (micro-array) expression data that enable wet-lab scientists to analyze their own data for biological insights. The number of programs and tools that have been developed over the last decade for transcriptomics is quite overwhelming and numerous websites provide portals for online analysis. On the other hand, the more sophisticated programs for dedicated bioinformaticians often prove quite a challenge for wet-lab scientists as they require sufficient skills in computer programming. We have assembled a set of programs that allows analysis of transcriptome data from a variety of angles at three levels. Furthermore, we provide a workshop for EVGN scientists to come and work with us on their own data in Amsterdam. This will enable them to better work efficiently with these tools in their home-lab and to communicate better with in-house bioinformatics/genomics facilities. There are three levels of analysis provided.

Level1: primary statistical analysis

Includes quality control of private and/or public datasets (downloaded from GEO (NCBI) or ArrayExpress (EBI), and analysis of statistical significance of differential gene expression.

Programs include: Cyber-T, Rosetta Resolver ANOVA, FDR-corrections, SAM, PAM

Level 2: clustering, pathway and literature-mining

Clustering analysis allows visualization of large datasets in a comprehensive manner to unveil underlying order in the datasets, i.e. coregulation of genes that have similar functions or transcription regulation or are relevant for classification of patients or animal groups.

Pathway analysis by various methods unveils order in the data by arranging them into either specific metabolic or signaling pathways from a variety of databases (KEGG, BioCarta, Reactome) or into Gene Ontology networks (www.geneontology.org).

Literature-mining provides quick reference to PubMed by ordering gene-gene or protein-protein interactions in networks of co-publications and relations to specific biological and clinical processes, thereby greatly shortening time spent on tracing literature related to your gene set.

Programs include: GSEA, GenMAPP, Bibliosphere, Ingenuity, DAVID, GOTM, Compendium

Level 3: transcriptional networks analysis

Transcriptome analysis most directly probes for regulation of gene expression. Coupling co-expression data to promoter sequences allows for analysis of causative sets of specific transcription factors and gene expression networks controlled by them. This is greatly facilitated by phylogenetic analysis as bio2

underlying regulatory sequences are conserved among species.

Programs include: VISTA, Genomatix Suite

Workshop

The workshop is open for members of EVGN several times a year. More information and information on how to apply can be found on the member’s intranet site.

This platform is managed by Anton Horrevoets at AMC.

Dowload out leaflet here

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