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Bioinformatics Advance Access originally published online on July 14, 2005
Bioinformatics 2005 21(17):3575-3577; doi:10.1093/bioinformatics/bti574
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goCluster integrates statistical analysis and functional interpretation of microarray expression data

Gunnar Wrobel , Frédéric Chalmel and Michael Primig *

Biozentrum and Swiss Institute of Bioinformatics Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland

*To whom correspondence should be addressed.

Motivation: Several tools that facilitate the interpretation of transcriptional profiles using gene annotation data are available but most of them combine a particular statistical analysis strategy with functional information. goCluster extends this concept by providing a modular framework that facilitates integration of statistical and functional microarray data analysis with data interpretation.

Results: goCluster enables scientists to employ annotation information, clustering algorithms and visualization tools in their array data analysis and interpretation strategy. The package provides four clustering algorithms and GeneOntology terms as prototype annotation data. The functional analysis is based on the hypergeometric distribution whereby the Bonferroni correction or the false discovery rate can be used to correct for multiple testing. The approach implemented in goCluster was successfully applied to interpret the results of complex mammalian and yeast expression data obtained with high density oligonucleotide microarrays (GeneChips).

Availability: goCluster is available via the BioConductor portal at www.bioconductor.org. The software package, detailed documentation, user- and developer guides as well as other background information are also accessible via a web portal at http://www.bioz.unibas.ch/gocluster.

Contact: michael.primig{at}unibas.ch

Received on February 17, 2005; revised on May 9, 2005; accepted on July 5, 2005

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