ArticleBMC bioinformatics2008
DISCLOSE : DISsection of CLusters Obtained by SEries of transcriptome data using functional annotations and putative transcription factor binding sites.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Advances in human papillomavirus detection for cervical cancer screening and diagnosis: challenges of conventional methods and opportunities for emergent tools.Analytical methods : advancing methods and applications · 2025Review
- A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae.Journal of visualized experiments : JoVE · 2015Article
- EfaR is a major regulator of Enterococcus faecalis manganese transporters and influences processes involved in host colonization and infection.Infection and immunity · 2013Article
- The transcriptional and gene regulatory network of Lactococcus lactis MG1363 during growth in milk.PloS one · 2013Article
- Using SCOPE to identify potential regulatory motifs in coregulated genes.Journal of visualized experiments : JoVE · 2011Article
- Time-resolved transcriptomics and bioinformatic analyses reveal intrinsic stress responses during batch culture of Bacillus subtilis.PloS one · 2011Article
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Authors and funding
6 authors.
Funding
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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.
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