metaModules identifies key functional subnetworks in microbiome-related disease
Bioinformatics p. 1- 8
Motivation: The human microbiome plays a key role in health and disease. Thanks to comparative metatranscriptomics, the cellular functions that are deregulated by the microbiome in disease can now be computationally explored. Unlike gene-centric approaches, pathway-based methods pro- vide a systemic view of such functions; however, they typically consider each pathway in isolation and in its entirety. They can therefore overlook the key differences that (i) span multiple pathways, (ii) contain bidirectionally deregulated components, (iii) are confined to a pathway region. To cap- ture these properties, computational methods that reach beyond the scope of predefined pathways are needed. Results: By integrating an existing module discovery algorithm into comparative metatranscrip- tomic analysis, we developed metaModules, a novel computational framework for automated iden- tification of the key functional differences between health- and disease-associated communities. Using this framework, we recovered significantly deregulated subnetworks that were indeed recog- nized to be involved in two well-studied, microbiome-mediated oral diseases, such as butanoate production in periodontal disease and metabolism of sugar alcohols in dental caries. More import- antly, our results indicate that our method can be used for hypothesis generation based on auto- mated discovery of novel, disease-related functional subnetworks, which would otherwise require extensive and laborious manual assessment. Availability and implementation: metaModules is available at https://bitbucket.org/alimay/metamodules/ Contact: firstname.lastname@example.org or email@example.com Supplementary information: Supplementary data are available at Bioinformatics online
|Life Sciences (theme 5)|
|Organisation||Life Sciences and Health|
May, A, Brandt, B.W, El-Kebir, M, Klau, G.W, Zaura, E, Crielaard, W, … Abeln, S. (2015). metaModules identifies key functional subnetworks in microbiome-related disease. Bioinformatics, 1–8.