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Nucleic Acids Research Advance Access originally published online on May 29, 2009
Nucleic Acids Research 2009 37(Web Server issue):W587-W592; doi:10.1093/nar/gkp435
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Nucleic Acids Research, 2009, Vol. 37, No. suppl_2 W587-W592
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Articles

VisHiC—hierarchical functional enrichment analysis of microarray data

Darya Krushevskaya1,2, Hedi Peterson3,4, Jüri Reimand1, Meelis Kull1,4 and Jaak Vilo1,2,4,*

1Institute of Computer Science, University of Tartu, Liivi 2, 50409 Tartu, 2Estonian Biocentre, 3Institute of Molecular and Cell Biology, University of Tartu, Riia 23, 51010 Tartu, and 4Quretec, Ülikooli 6a, 51003 Tartu, Estonia

*To whom correspondence should be addressed. Tel: +372 50 49 365; Fax: +372 737 5468; Email: vilo{at}ut.ee; vilo{at}quretec.com

Received February 22, 2009. Revised April 20, 2009. Accepted May 11, 2009.

Measuring gene expression levels with microarrays is one of the key technologies of modern genomics. Clustering of microarray data is an important application, as genes with similar expression profiles may be regulated by common pathways and involved in related functions. Gene Ontology (GO) analysis and visualization allows researchers to study the biological context of discovered clusters and characterize genes with previously unknown functions. We present VisHiC (Visualization of Hierarchical Clustering), a web server for clustering and compact visualization of gene expression data combined with automated function enrichment analysis. The main output of the analysis is a dendrogram and visual heatmap of the expression matrix that highlights biologically relevant clusters based on enriched GO terms, pathways and regulatory motifs. Clusters with most significant enrichments are contracted in the final visualization, while less relevant parts are hidden altogether. Such a dense representation of microarray data gives a quick global overview of thousands of transcripts in many conditions and provides a good starting point for further analysis. VisHiC is freely available at http://biit.cs.ut.ee/vishic.


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