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Publications (2)0 Total impact

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    ABSTRACT: Position frequency matrices (PFMs) represent the most widely employed model for transcription factor binding motifs (TFBMs). Given a set of TFBMs, regulatory networks can be predicted through in silico methods by using cognate binding sequences to construct models. The availability of PFMs offers much promise for high throughput TFBM detection in the form of a pipeline to facilitate the analysis of microarray data. Here we present BiSAn, a simple yet effective software for biologists to efficiently identify putative TFBMs from promoter sites belonging to their genes of interest at the click of a button. BiSAn is freely available and can be downloaded from
    Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009. 3rd International Conference on; 07/2009
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    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
    In silico biology 02/2008; 8(3-4):363-76.