FUNN-MG: A Metagenomic Systems Biology Computational Framework
Microorganisms abound everywhere. Though we know they play key roles in several ecosystems, too little is known about how these complex communities work. To act as a community they must interact with each other in order to achieve such community stability in which proper functions allows the microbial community to adapt in complex environment conditions. Thus, to effectively understand microbial genetic networks one needs to explore them by means of a systems biology approach. The proposed approach extends the metagenomic gene-centric view by taking into account the set of genes present in a metagenome and also the complex links of interactions among these genes and by treating the microbiome as a single biological system. In this paper, we present the FUNN-MG computational framework to explore functional modules in microbial genetic networks.
Keywordssystems biology gene and pathway enrichment analysis graph representation graph visualization metagenomics
Unable to display preview. Download preview PDF.
- 1.NCBI: Metagenomics: Sequences from the environment [internet]. Sequences from the Environment, Tyson (2013)Google Scholar
- 3.Moriya, Y., Itoh, M., Okuda, S., Yoshizawa, A.C., Kanehisa, M.: KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic acids research 35(Web Server issue), W182–W185 (2007)Google Scholar
- 4.Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., Tanabe, M.: KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Research 40(Database issue), D109–D114 (2012)Google Scholar
- 5.Tenenbaum, D.: KEGGREST: Client-side REST access to KEGG. R package version 1.0.1Google Scholar