Dataset Information

DynaMod: dynamic functional modularity analysis.

ABSTRACT: A comprehensive analysis of enriched functional categories in differentially expressed genes is important to extract the underlying biological processes of genome-wide expression profiles. Moreover, identification of the network of significant functional modules in these dynamic processes is an interesting challenge. This study introduces DynaMod, a web-based application that identifies significant functional modules reflecting the change of modularity and differential expressions that are correlated with gene expression profiles under different conditions. DynaMod allows the inspection of a wide variety of functional modules such as the biological pathways, transcriptional factor-target gene groups, microRNA-target gene groups, protein complexes and hub networks involved in protein interactome. The statistical significance of dynamic functional modularity is scored based on Z-statistics from the average of mutual information (MI) changes of involved gene pairs under different conditions. Significantly correlated gene pairs among the functional modules are used to generate a correlated network of functional categories. In addition to these main goals, this scoring strategy supports better performance to detect significant genes in microarray analyses, as the scores of correlated genes show the superior characteristics of the significance analysis compared with those of individual genes. DynaMod also offers cross-comparison between different analysis outputs. DynaMod is freely accessible at http://piech.kaist.ac.kr/dynamod.

SUBMITTER: Sun CH

PROVIDER: S-EPMC2896096 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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DynaMod: dynamic functional modularity analysis.

Sun Choong-Hyun CH Hwang Taeho T Oh Kimin K Yi Gwan-Su GS

Nucleic acids research 20100511 Web Server issue

A comprehensive analysis of enriched functional categories in differentially expressed genes is important to extract the underlying biological processes of genome-wide expression profiles. Moreover, identification of the network of significant functional modules in these dynamic processes is an interesting challenge. This study introduces DynaMod, a web-based application that identifies significant functional modules reflecting the change of modularity and differential expressions that are corre ...[more]

PMID: 20460468

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Project description:In biological networks of molecular interactions in a cell, network motifs that are biologically relevant are also functionally coherent, or form functional modules. These functionally coherent modules combine in a hierarchical manner into larger, less cohesive subsystems, thus revealing one of the essential design principles of system-level cellular organization and function-hierarchical modularity. Arguably, hierarchical modularity has not been explicitly taken into consideration by most, if not all, functional annotation systems. As a result, the existing methods would often fail to assign a statistically significant functional coherence score to biologically relevant molecular machines. We developed a methodology for hierarchical functional annotation. Given the hierarchical taxonomy of functional concepts (e.g., Gene Ontology) and the association of individual genes or proteins with these concepts (e.g., GO terms), our method will assign a Hierarchical Modularity Score (HMS) to each node in the hierarchy of functional modules; the HMS score and its p-value measure functional coherence of each module in the hierarchy. While existing methods annotate each module with a set of "enriched" functional terms in a bag of genes, our complementary method provides the hierarchical functional annotation of the modules and their hierarchically organized components. A hierarchical organization of functional modules often comes as a bi-product of cluster analysis of gene expression data or protein interaction data. Otherwise, our method will automatically build such a hierarchy by directly incorporating the functional taxonomy information into the hierarchy search process and by allowing multi-functional genes to be part of more than one component in the hierarchy. In addition, its underlying HMS scoring metric ensures that functional specificity of the terms across different levels of the hierarchical taxonomy is properly treated. We have evaluated our method using Saccharomyces cerevisiae data from KEGG and MIPS databases and several other computationally derived and curated datasets. The code and additional supplemental files can be obtained from http://code.google.com/p/functional-annotation-of-hierarchical-modularity/ (Accessed 2012 March 13).

Dataset Information

DynaMod: dynamic functional modularity analysis.

Publications

DynaMod: dynamic functional modularity analysis.

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