Large Scale Gene Expression Analysis in Cardiovacular and Nutritional Research at Bigcat

Large scale gene expression analysis in cardiovacular and nutritional research at BiGCaT Bioinformatics in Maastricht.

Chris T.A. Evelo, Stan Gaj, Edwin G.W. ter Voert, Rachel I.M. van Haaften, Peter A.J. Hilbers (a), Wim H.M. Saris (b), and Harry A.J. Struijker-Boudier (c)

BiGCaT Bioinformatics BMT-TU/e & UM a: Department of Biomedical Engineering; Universiteit Eindhoven b: Nutrition and Toxicology Research Institute Maastricht c: Cardiovascular Research Institute Maastricht

BIGCaT bioinformatics is a joint taskforce from the Universiteit Maastricht and the Technische Universiteit Eindhoven. BiGCaTs main goal is to reach better understanding of important cardiovascular processes and effects of nutrition on gene expression through improvement of functional analysis of large-scale gene expression experiments. Microarrays from Affymetrix, Agilent and Incyte as well as home spotted cDNA and oligo arrays are used. Main steps in the analysis are: 1. Perl script automated reporter annotation, where possible with well-described proteins (Swissprot IDs) using cross links obtained from BLAST searches against Unigene and reorganized EMBL databases or using BLASTp searches in a subdivided Swissprot database with filtering of multiple hits resulting from frame shift errors in reporter sequences and validation of partial coding sequence through sequence alignments of the EMBL cross links from Swissprot (see also the second abstract by Stan Gaj et. al.). 2. Java coded statistical treatments of replicates and flipped hybridizations and normalization with respect to expression levels using trend analysis and polynomial fits. 3. Customized clustering of expression patterns and cluster visualization meant to assist in manual selection of the most promising clustering techniques, the number of clusters to use and the most interesting clusters. Combined these three initial steps allow us to find the most strongly regulated genes, to check individual interesting genes or groups of genes and to browse clusters of genes with interesting expression patterns, this is however still just a first step to understanding. 4. To improve this, expression patterns and clusters can be mapped to functional pathways using GenMapp, allowing conclusions like: the cluster with early up regulated genes contains a large number of apoptosis related genes, meaning that apoptosis occurs during early in the study. It also allows for in-silico experiments where we can map expression patterns to chromosomal regions or can visualize common transcription factor binding sites in expression clusters.