Functional Genomics Assistant (FUGA): a toolbox for the analysis of complex biological networks
1 Cardiovascular Division - King's College London (KCL) BHF Centre of Research Excellence - School of Medicine - James Black Centre - 125 Coldharbour Lane, London SE5 9NU - UK
2 Centre for Bioinformatics - Department of Informatics - School of Natural & Mathematical Sciences, King's College London (KCL) - Strand, London WC2R 2LS - UK
3 Computational Genomics Unit, Institute of Agrobiotechnology - Centre for Research & Technology Hellas - Thessaloniki - Greece
4 Donnelly Centre for Cellular & Biomolecular Research - University of Toronto - 160 College Street, Toronto, Ontario M5S 3E1 - Canada
Citation and License
BMC Research Notes 2011, 4:462 doi:10.1186/1756-0500-4-462Published: 28 October 2011
Cellular constituents such as proteins, DNA, and RNA form a complex web of interactions that regulate biochemical homeostasis and determine the dynamic cellular response to external stimuli. It follows that detailed understanding of these patterns is critical for the assessment of fundamental processes in cell biology and pathology. Representation and analysis of cellular constituents through network principles is a promising and popular analytical avenue towards a deeper understanding of molecular mechanisms in a system-wide context.
We present Functional Genomics Assistant (FUGA) - an extensible and portable MATLAB toolbox for the inference of biological relationships, graph topology analysis, random network simulation, network clustering, and functional enrichment statistics. In contrast to conventional differential expression analysis of individual genes, FUGA offers a framework for the study of system-wide properties of biological networks and highlights putative molecular targets using concepts of systems biology.
FUGA offers a simple and customizable framework for network analysis in a variety of systems biology applications. It is freely available for individual or academic use at http://code.google.com/p/fuga webcite.