Literature DB >> 33372654

Metage2Metabo, microbiota-scale metabolic complementarity for the identification of key species.

Arnaud Belcour1, Clémence Frioux1,2,3,4, Méziane Aite1, Anthony Bretaudeau1,5,6, Falk Hildebrand3,4, Anne Siegel1.   

Abstract

To capture the functional diversity of microbiota, one must identify metabolic functions and species of interest within hundreds or thousands of microorganisms. We present Metage2Metabo (M2M) a resource that meets the need for de novo functional screening of genome-scale metabolic networks (GSMNs) at the scale of a metagenome, and the identification of critical species with respect to metabolic cooperation. M2M comprises a flexible pipeline for the characterisation of individual metabolisms and collective metabolic complementarity. In addition, M2M identifies key species, that are meaningful members of the community for functions of interest. We demonstrate that M2M is applicable to collections of genomes as well as metagenome-assembled genomes, permits an efficient GSMN reconstruction with Pathway Tools, and assesses the cooperation potential between species. M2M identifies key organisms by reducing the complexity of a large-scale microbiota into minimal communities with equivalent properties, suitable for further analyses.
© 2020, Belcour et al.

Entities:  

Keywords:  computational biology; human; keystone species; metabolic complementarity; metabolic modelling; metagenomics; microbiota; systems biology

Mesh:

Year:  2020        PMID: 33372654      PMCID: PMC7861615          DOI: 10.7554/eLife.61968

Source DB:  PubMed          Journal:  Elife        ISSN: 2050-084X            Impact factor:   8.140


  96 in total

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Journal:  Proc Natl Acad Sci U S A       Date:  2019-09-06       Impact factor: 11.205

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Journal:  BMC Genomics       Date:  2018-05-25       Impact factor: 3.969

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10.  MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies.

Authors:  Dongwan D Kang; Feng Li; Edward Kirton; Ashleigh Thomas; Rob Egan; Hong An; Zhong Wang
Journal:  PeerJ       Date:  2019-07-26       Impact factor: 2.984

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