Ali May1, Bernd W Brandt2, Mohammed El-Kebir3, Gunnar W Klau4, Egija Zaura2, Wim Crielaard2, Jaap Heringa5, Sanne Abeln5. 1. Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, Amsterdam, The Netherlands, Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, Amsterdam, The Netherlands, Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, Amsterdam, The Netherlands. 2. Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, Amsterdam, The Netherlands. 3. Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, Amsterdam, The Netherlands, Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, USA and Life Sciences, Centre for Mathematics and Computer Science (CWI), Amsterdam, The Netherlands. 4. Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, Amsterdam, The Netherlands, Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, Amsterdam, The Netherlands, Life Sciences, Centre for Mathematics and Computer Science (CWI), Amsterdam, The Netherlands. 5. Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, Amsterdam, The Netherlands, Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, Amsterdam, The Netherlands.
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. AVAILABILITY AND IMPLEMENTATION: metaModules is available at https://bitbucket.org/alimay/metamodules/ CONTACT: a.may@vu.nl or s.abeln@vu.nl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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. AVAILABILITY AND IMPLEMENTATION: metaModules is available at https://bitbucket.org/alimay/metamodules/ CONTACT: a.may@vu.nl or s.abeln@vu.nl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Ranjith Rajendran; Ali May; Leighann Sherry; Ryan Kean; Craig Williams; Brian L Jones; Karl V Burgess; Jaap Heringa; Sanne Abeln; Bernd W Brandt; Carol A Munro; Gordon Ramage Journal: Sci Rep Date: 2016-10-21 Impact factor: 4.379