MaXlab: a novel application for the cross comparison and integration of biological signatures from microarray studies.
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.
Khalid
Sabah
S
Molecular Immunology/Bioinformatics Group, Microarray Facility, Division of BioSciences, Brunel University, Uxbridge, UK.
Khan
Mohsin
M
Gorle
Chandrasekhar Babu
CB
Fraser
Karl
K
Wang
Ping
P
Liu
Xiaohui
X
Li
Suling
S
D000465:Algorithms; D001185:Artificial Intelligence; D001327:Autoimmune Diseases / Q000235:genetics; D001943:Breast Neoplasms / Q000235:genetics; D003882:Dermatomyositis / Q000235:genetics; D020869:Gene Expression Profiling; D006801:Humans; D007108:Immune Tolerance / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software