%0 Journal Article
%T DISCLOSE : DISsection of CLusters Obtained by SEries of transcriptome data using functional annotations and putative transcription factor binding sites
%A Evert-Jan Blom
%A Sacha AFT van Hijum
%A Klaas J Hofstede
%A Remko Silvis
%A Jos BTM Roerdink
%A Oscar P Kuipers
%J BMC Bioinformatics
%D 2008
%I BioMed Central
%R 10.1186/1471-2105-9-535
%X We developed an exploratory application that benchmarks the results of clustering methods using functional annotations. In addition, a de novo DNA motif discovery algorithm is integrated in our program which identifies overrepresented DNA binding sites in the upstream DNA sequences of genes from the clusters that are indicative of sites of transcriptional control. The performance of our program was evaluated by comparing the original results of a time course experiment with the findings of our application.DISCLOSE assists researchers in the prokaryotic research community in systematically evaluating results of the application of a range of clustering algorithms to transcriptome data. Different performance measures allow to quickly and comprehensively determine the best suited clustering approach for a given dataset.DNA microarray technology is commonly used to study mRNA expression levels of genes under different experimental conditions. Clustering approaches are widely used in the analysis of gene expression data. The ability to identify groups of genes exhibiting similar expression patterns by clustering allows for detailed biological insights into global regulation of gene expression and cellular processes. Clustering methodology is considered a potent means to infer putative gene function [1,2].In the process of the analysis of transcriptome data, researchers are often faced with the choice between a wide variety of clustering methods and associated parameters. The results of the application of different clustering algorithms to the same dataset will place genes in different clusters and therefore result in different biological interpretations of the same dataset. Moreover, selecting the most appropriate clustering method and parameters heavily depends on the experience of the researcher and on the nature of the dataset analyzed.Several studies have shown the relevance of applying external measures (i.e., using prior biological knowledge) to more objectively eva
%U http://www.biomedcentral.com/1471-2105/9/535