metaModules identifies key functional subnetworks in microbiome-related disease

Abstract : 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 provide 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 capture these properties, computational methods that reach beyond the scope of predefined pathways are needed Results: By integrating an existing module discovery algorithm into comparative metatranscriptomic analysis, we developed metaModules, a novel computational framework for automated identification of the key functional differences between health- and disease-associated communities. Using this framework, we recovered significantly deregulated subnetworks that were indeed recognized 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 importantly, our results indicate that our method can be used for hypothesis generation based on automated discovery of novel, disease-related functional subnetworks, which would otherwise require extensive and laborious manual assessment.
Type de document :
Article dans une revue
Bioinformatics, Oxford University Press (OUP), 2016, 32 (11), pp.1678 - 1685. 〈10.1093/bioinformatics/btv526〉
Liste complète des métadonnées
Contributeur : Marie-France Sagot <>
Soumis le : jeudi 27 octobre 2016 - 10:44:33
Dernière modification le : dimanche 29 octobre 2017 - 20:26:01

Lien texte intégral




Ali May, Bernd W. Brandt, Mohammed El-Kebir, Gunnar W. Klau, Egija Zaura, et al.. metaModules identifies key functional subnetworks in microbiome-related disease. Bioinformatics, Oxford University Press (OUP), 2016, 32 (11), pp.1678 - 1685. 〈10.1093/bioinformatics/btv526〉. 〈hal-01388508〉



Consultations de la notice