Array2BIO: from microarray expression data to functional annotation of co-regulated genes - PubMed (original) (raw)
Array2BIO: from microarray expression data to functional annotation of co-regulated genes
Gabriela G Loots et al. BMC Bioinformatics. 2006.
Abstract
Background: There are several isolated tools for partial analysis of microarray expression data. To provide an integrative, easy-to-use and automated toolkit for the analysis of Affymetrix microarray expression data we have developed Array2BIO, an application that couples several analytical methods into a single web based utility.
Results: Array2BIO converts raw intensities into probe expression values, automatically maps those to genes, and subsequently identifies groups of co-expressed genes using two complementary approaches: (1) comparative analysis of signal versus control and (2) clustering analysis of gene expression across different conditions. The identified genes are assigned to functional categories based on Gene Ontology classification and KEGG protein interaction pathways. Array2BIO reliably handles low-expressor genes and provides a set of statistical methods for quantifying expression levels, including Benjamini-Hochberg and Bonferroni multiple testing corrections. An automated interface with the ECR Browser provides evolutionary conservation analysis for the identified gene loci while the interconnection with Crème allows prediction of gene regulatory elements that underlie observed expression patterns.
Conclusion: We have developed Array2BIO - a web based tool for rapid comprehensive analysis of Affymetrix microarray expression data, which also allows users to link expression data to Dcode.org comparative genomics tools and integrates a system for translating co-expression data into mechanisms of gene co-regulation. Array2BIO is publicly available at http://array2bio.dcode.org.
Figures
Figure 1
Schematic flowchart of the Array2BIO analysis.
Figure 2
Array2BIO automatically fetches KEGG maps (Ogata et al. 1999) from the KEGG web site and utilizes locally generated data to color-demarcate individual genes. KEGG snapshot of cytokine-cytokine receptor interactions that are related to the Y. pestis infection with identified genes in red.
Figure 3
SNOMAD local Z-test for handling low-expressors. Signal versus control fold different in expression is plotted against the median signal and control expression. Orange dots represent over- and under-expressors.
Figure 4
Visualization of clustering analysis. A full clustering tree across 5 control (cN) and 5 signal (sN) conditions (A) and a zoom in into two genes (B). The zoom in function is performed by clicking on a region in the full clustering tree, in this case, depicted by the orange frame.
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