Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification

Author:

Gautam Anupam1234,Bhowmik Debaleena56,Basu Sayantani7,Zeng Wenhuan128,Lahiri Abhishake6910,Huson Daniel H1234ORCID,Paul Sandip10ORCID

Affiliation:

1. Algorithms in Bioinformatics , Institute for Bioinformatics and Medical Informatics, , Tübingen , Germany

2. University of Tübingen , Institute for Bioinformatics and Medical Informatics, , Tübingen , Germany

3. International Max Planck Research School “From Molecules to Organisms”, Max Planck Institute for Biology Tübingen , Tübingen , Germany

4. Cluster of Excellence: EXC 2124: Controlling Microbes to Fight Infection , Tübingen , Germany

5. Cell Biology and Physiology Division, CSIR-Indian Institute of Chemical Biology , Kolkata , India

6. Academy of Scientific and Innovative Research (AcSIR) , Ghaziabad 201002 , India

7. Department of Computer Science, University of Illinois at Urbana-Champaign , Urbana, IL 61801 , United States

8. Cluster of Excellence: EXC 2064: Machine Learning: New Perspectives for Science, University of Tübingen , Tübingen , Germany

9. Infectious Diseases and Immunology Division, CSIR-Indian Institute of Chemical Biology , Kolkata , India

10. Centre for Health Science and Technology, JIS Institute of Advanced Studies and Research Kolkata, JIS University , West Bengal , India

Abstract

Abstract A microbial community maintains its ecological dynamics via metabolite crosstalk. Hence, knowledge of the metabolome, alongside its populace, would help us understand the functionality of a community and also predict how it will change in atypical conditions. Methods that employ low-cost metagenomic sequencing data can predict the metabolic potential of a community, that is, its ability to produce or utilize specific metabolites. These, in turn, can potentially serve as markers of biochemical pathways that are associated with different communities. We developed MMIP (Microbiome Metabolome Integration Platform), a web-based analytical and predictive tool that can be used to compare the taxonomic content, diversity variation and the metabolic potential between two sets of microbial communities from targeted amplicon sequencing data. MMIP is capable of highlighting statistically significant taxonomic, enzymatic and metabolic attributes as well as learning-based features associated with one group in comparison with another. Furthermore, MMIP can predict linkages among species or groups of microbes in the community, specific enzyme profiles, compounds or metabolites associated with such a group of organisms. With MMIP, we aim to provide a user-friendly, online web server for performing key microbiome-associated analyses of targeted amplicon sequencing data, predicting metabolite signature, and using learning-based linkage analysis, without the need for initial metabolomic analysis, and thereby helping in hypothesis generation.

Funder

Science and Engineering Research Board

Indian Council of Medical Research, Government of India

High Performance and Cloud Computing Group at the Zentrum für Datenverarbeitung of the University of Tübingen

Deutsche Forschungsgemeinschaft

German Network for Bioinformatics Infrastructure

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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