GraphWeb: mining heterogeneous biological networks for gene modules with functional significance.
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/.
Reimand
Jüri
J
University of Tartu, Institute of Computer Science, Liivi 2, Tartu, Estonia.
Tooming
Laur
L
Peterson
Hedi
H
Adler
Priit
P
Vilo
Jaak
J
D018797:Cell Cycle Proteins / Q000235:genetics / Q000378:metabolism; D003196:Computer Graphics; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D020407:Internet; D025941:Protein Interaction Mapping; D012984:Software
D018797:Cell Cycle Proteins