DISCLOSE : DISsection of CLusters Obtained by SEries of transcriptome data using functional annotations and putative transcription factor binding sites

Evert-Jan Blom,Oscar P Kuipers,Sacha Aft Van Hijum,Klaas J Hofstede,Jos Btm Roerdink,Remko Silvis

doi:10.1186/1471-2105-9-535

Abstract

BackgroundA typical step in the analysis of gene expression data is the determination of clusters of genes that exhibit similar expression patterns. Researchers are confronted with the seemingly arbitrary choice between numerous algorithms to perform cluster analysis.ResultsWe 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.ConclusionDISCLOSE 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.

Highlights

A typical step in the analysis of gene expression data is the determination of clusters of genes that exhibit similar expression patterns
Discussion of the results The results of our DISCLOSE analysis show that we were able to identify most of the functional overrepresented categories that are discussed in the original study
A number of functional categories were not recovered using our approach due to the nature of the analysis employed in the original study which was based on anal

Summary

Introduction

A typical step in the analysis of gene expression data is the determination of clusters of genes that exhibit similar expression patterns. Researchers are confronted with the seemingly arbitrary choice between numerous algorithms to perform cluster analysis. 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. 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 (page number not for citation purposes)

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Results

Discussion

Conclusion