Affiliations: Molecular Immunology/Bioinformatics Group, Microarray
Facility, Division of BioSciences, Brunel University, Uxbridge, UB8 3PH, UK | Intelligent Data Analysis Group, Department of
Information Systems and Computing, Brunel University, Uxbridge, UB8 3PH, UK | Medical Oncology Unit, Institute of Cancer, Barts and
London, Queen Mary School of Medicine, London, UK | Immunology Group, Institute of Cell and Molecular
Sciences, Barts and London, Queen Mary School of Medicine, London, UK
Abstract: Microarray gene expression datasets are continually being placed in
public repositories. As a result, one of the most important emerging challenges
is that which enables researchers to take full advantage of such previously
accumulated data to discover or validate common genes in similar biological
systems. In light of this we have designed the MaXlab software to not only
cross-compare available array data from different laboratories but also extract
further knowledge from gene expression patterns embedded within published data.
More importantly MaXlab offers a flexible and automated solution applicable for
microarray technologies including cDNA and Affymetrix gene chips generating
expression profiles for common genes with biological significance. We have
identified several sets of genes previously unknown to be commonly expressed
across studies investigating related biological questions. Among them is the
identification of 17 genes involved in the dysregulation of immune tolerance
including the crucial transcription factor Egr2. In addition, we have
identified 175 genes commonly expressed in basal and luminal breast tumours in
response to the chemotherapeutic drug doxorubicin. The universal expression and
characterisation of these encouraging genes identified through MaXlab suggests
that they may play a common role in the mechanism of disease and hence act as
an incentive for further investigation for identifying potential therapeutic
targets. Overall, MaXlab is an attractive application for molecular biologists
extracting the intersection between microarray datasets together with the gene
expression profiles, from which biologists are able to infer further biological
insights. The software together with file formats and additional material is
freely available at http://www.immuno-software.org.
