Abstract
BackgroundCellular 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.FindingsWe 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.ConclusionFUGA 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.
Highlights
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
Recently, Functional Genomics Assistant (FUGA) functionality was applied on experimentally derived datasets to define a population-based miRNA signature of type 2 diabetes [11], elucidate the expression patterns of iron regulatory protein 2 (IRP2) [12], and characterize genome-wide expression patterns in physiological cardiac hypertrophy [13]
Network reconstruction Biological networks can be inferred with FUGA from computationally or experimentally derived datasets (e.g. BLAST similarity matrices, microarray data) using any form of similarity measure (e.g. Pearson Correlation Coefficient or PCC) to define pairs of nodes
Summary
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. Conclusion: FUGA offers a simple and customizable framework for network analysis in a variety of systems biology applications. The treatment of biological data as graphs, where typically nodes signify cellular entities (e.g. genes, proteins, metabolites) and connections (edges) denote
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call