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Nucleic Acids Research Advance Access published online on May 6, 2008
Nucleic Acids Research, doi:10.1093/nar/gkn230
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© 2008 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.

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GraphWeb: mining heterogeneous biological networks for gene modules with functional significance

Jüri Reimand1,2, Laur Tooming1, Hedi Peterson3,4, Priit Adler3 and Jaak Vilo1,4,*

1University of Tartu, Institute of Computer Science, Liivi 2, Tartu, Estonia, 2EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, 3University of Tartu, Institute of Molecular and Cell Biology, Riia 23a and 4QureTec Ltd. Ülikooli 6a, 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 January 31, 2008. Revised April 3, 2008. Accepted April 11, 2008.

Deciphering heterogeneous cellular networks with embedded modules is a great challenge of current systems biology. Experimental and computational studies construct complex networks of molecules that describe various aspects of the cell such as transcriptional regulation, protein interactions and metabolism. Groups of interacting genes and proteins reflect network modules that potentially share regulatory mechanisms and relate to common function. Here, we present GraphWeb, a public web server for biological network analysis and module discovery. GraphWeb provides methods to: (1) integrate heterogeneous and multispecies data for constructing directed and undirected, weighted and unweighted networks; (ii) discover network modules using a variety of algorithms and topological filters and (iii) interpret modules using functional knowledge of the Gene Ontology and pathways, as well as regulatory features such as binding motifs and microRNA targets. GraphWeb is designed to analyse individual or multiple merged networks, search for conserved features across multiple species, mine large biological networks for smaller modules, discover novel candidates and connections for known pathways and compare results of high-throughput datasets. The GraphWeb is available at http://biit.cs.ut.ee/graphweb/.

The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.


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