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


RESULTS
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.


CONCLUSIONS
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.
Blom
Evert-Jan
EJ
Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, The Netherlands. e.j.blom@rug.nl
van Hijum
Sacha A F T
SA
Hofstede
Klaas J
KJ
Silvis
Remko
R
Roerdink
Jos B T M
JB
Kuipers
Oscar P
OP
D000465:Algorithms; D001665:Binding Sites; D016000:Cluster Analysis; D004247:DNA / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software; D014157:Transcription Factors / Q000378:metabolism*
D014157:Transcription Factors; D004247:DNA