Genome Expression Pathway Analysis Tool

Gene expression analysis with microarrays opened a new insight in the living cell, revolutionizing biological research in many fields. Although there exist a large number of data analysis tools available for these steps, most of them lack the ability to help with the interpretation of the results. For this reason we developed a web-based tool, Genome Expression Pathway Analysis Tool (GEPAT), offering an integrated analysis of transcriptome data under genomic, proteomic and metabolic context.

Data Import

GEPAT imports various formats of oligonucleotide arrays, cDNA arrays and data tables. At the moment many human microarray chips are supported, more chips and species will follow. A gene annotation database was build based on the UniGene and Ensembl databases, allowing gene identification for the probes on the microarray chip. Missing value imputation, background correction and normalization methods can be applied to the data.

Gene Information

For an interpretation of the result genes, GEPAT uses data from the Ensembl database and provides information about gene names, chromosomal location, GO categories and enzymatic activity for each probe on the chip. Gene interaction and association data from the STRING database, overlaid with analysis results, e.g. fold change values, can be used to find functionally related genes. SMART protein domain information and literature references are available as well.

Data Analysis

Our tool offers various analysis methods for gene expression data. Hierarchical, k-means and PCA clustering methods allow group detection in probes and samples. With these detected groups or a predefined group set, a linear model based t-Test can be used to identify differences in gene expression between groups. An M/A-Plot, filtering and sorting on fold-change, p-value and probe variance allow a quick identification of differentially expressed genes.

Data Interpretation

GEPAT uses enzymatic information for the genes to overlay analysis results onto KEGG pathway maps, allowing an overview of the regulation of metabolic pathways. To check for chromosomal aberrations a chromosome overview can be generated, showing analysis results and optional CGH data on the chromosome set. For a further investigation of gene functions, a tree view of the Gene Ontology graph has been implemented.