MicroPath-A pathway-based pipeline for the comparison of multiple gene expression profiles to identify common biological signatures

Mohsin Khan, Chandrasekhar Babu Gorle, Ping Wang, Xiao-Hui Liu, Su-Ling Li

Immunology Group, Institute of Cell and Molecular Sciences, Barts and London School of Medicine, London, UK..
Intelligent Data Analysis Group, Department of Information Systems and Computing.
Molecular Immunology & bioinformatics Group, Microarray Facility, Division of Bio-Sciences, Brunel University, Uxbridge, UB8 3PH, UK.

DOI: 10.4236/jbise.2009.22020                

Abstract

High throughput gene expression analysis is swiftly becoming the focal point for deciphering molecular mechanisms underlying various dif-ferent biological questions. Testament to this is the fact that vast volumes of expression profiles are being generated rapidly by scientists worldwide and subsequently stored in publicly available data repositories such as ArrayEx-press and the Gene Expression Omnibus (GEO). Such wealth of biological data has motivated biologists to compare expression profiles gen-erated from biologically-related microarray ex-periments in order to unravel biological mecha-nisms underlying various states of diseases. However, without the availability of appropriate software and tools, they are compelled to use manual or labour-intensive methods of com-parisons. A scrutiny of current literature makes it apparent that there is a soaring need for such bioinformatics tools that cater for the multiple analyses of expression profiles. In order to contribute towards this need, we have developed an efficient software pipeline for the analysis of multiple gene expression data-sets, called Micropath, which implements three principal functions; 1) it searches for common genes amongst n number of datasets using a number crunching method of comparison as well as applying the principle of permutations and combinations in the form of a search strat-egy, 2) it extracts gene expression patterns both graphically and statistically, and 3) it streams co-expressed genes to all molecular pathways belonging to KEGG in a live fashion. We sub-jected MicroPath to several expression datasets generated from our tolerance-related in-house microarray experiments as well as published data and identified a set of 31 candidate genes that were found to be co-expressed across all interesting datasets. Pathway analysis revealed their putative roles in regulating immune toler-ance. MicroPath is freely available to download from: www.1066technologies.co.uk/micropath.

Keywords

Co-Expression Analysis, Microarray, Permutations and Combinations, Multiple Gene Expression Analysis

Conflicts of Interest

The authors declare no conflicts of interest.

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