Functional Genomics Assistant (FUGA): a toolbox for the analysis of complex biological networks

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.

Relevance of TNBS-Colitis in Rats: A Methodological Study with Endoscopic, Histologic and Transcriptomic Characterization and Correlation to IBD

“…Subsequently, gene clusters were enriched for over-represented Gene Ontology (GO) Biological Process (BP) terms [35] using the hypergeometric test. For a cluster with n genes and an a priori defined functional category with K genes, the hypergeometric test was used to evaluate the significance of overlap k between the cluster and GO-BP category [36], [37]. All N genes on a microarray were used as reference.…”

The Identification of Gut Neuroendocrine Tumor Disease by Multiple Synchronous Transcript Analysis in Blood

“…Details of the microarray analyses and gene identification including normalization using Robust Multi-array Average (RMA) [18], identifying probes and mapping to Ensembl gene identifiers, assessment of gene co-expression network inferences, network partitioning and functional enrichment analyses [19] are included in the Supplemental methods. This computational approach ( Figure 1 , Figure S2 ) resulted in the identification of 75 candidate genes.…”

Molecular diagnostics in gastric cancer

“…Principal component analyses and hierarchical clustering enable functional classification of the identified gene expression networks and even individual genes and related signalling pathways (249). By this approach putative target genes with prognostic or therapeutic potential can be identified as well as further interacting partners (250,251). These target genes don't necessarily have to be deregulated below or above a certain threshold (252), comparison to "normal" tissue or between different entities of malignant diseases defines central node-genes like transcription factors or cell cycle regulators (253).…”

“…Computational visualisation of gene-geneinteraction networks and the related gene-clusters enables also a topological assessment of gene-coexpression as well as the integration of multiple datasets for a comprehensive analysis (251,260). The results are based on features of the ENSEMBL database and classified by "gene-ontology" terms: a) biological process, b) molecular function, c) cellular component (261).…”