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Scalable and exhaustive screening of metabolic functions carried out by microbial consortia
MOTIVATION: The selection of species exhibiting metabolic behaviors of interest is a challenging step when switching from the investigation of a large microbiota to the study of functions effectiveness. Approaches based on a compartmentalized framework are not scalable. The output of scalable approa...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6129287/ https://www.ncbi.nlm.nih.gov/pubmed/30423063 http://dx.doi.org/10.1093/bioinformatics/bty588 |
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author | Frioux, Clémence Fremy, Enora Trottier, Camille Siegel, Anne |
author_facet | Frioux, Clémence Fremy, Enora Trottier, Camille Siegel, Anne |
author_sort | Frioux, Clémence |
collection | PubMed |
description | MOTIVATION: The selection of species exhibiting metabolic behaviors of interest is a challenging step when switching from the investigation of a large microbiota to the study of functions effectiveness. Approaches based on a compartmentalized framework are not scalable. The output of scalable approaches based on a non-compartmentalized modeling may be so large that it has neither been explored nor handled so far. RESULTS: We present the Miscoto tool to facilitate the selection of a community optimizing a desired function in a microbiome by reporting several possibilities which can be then sorted according to biological criteria. Communities are exhaustively identified using logical programming and by combining the non-compartmentalized and the compartmentalized frameworks. The benchmarking of 4.9 million metabolic functions associated with the Human Microbiome Project, shows that Miscoto is suited to screen and classify metabolic producibility in terms of feasibility, functional redundancy and cooperation processes involved. As an illustration of a host-microbial system, screening the Recon 2.2 human metabolism highlights the role of different consortia within a family of 773 intestinal bacteria. AVAILABILITY AND IMPLEMENTATION: Miscoto source code, instructions for use and examples are available at: https://github.com/cfrioux/miscoto. |
format | Online Article Text |
id | pubmed-6129287 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-61292872018-09-12 Scalable and exhaustive screening of metabolic functions carried out by microbial consortia Frioux, Clémence Fremy, Enora Trottier, Camille Siegel, Anne Bioinformatics Eccb 2018: European Conference on Computational Biology Proceedings MOTIVATION: The selection of species exhibiting metabolic behaviors of interest is a challenging step when switching from the investigation of a large microbiota to the study of functions effectiveness. Approaches based on a compartmentalized framework are not scalable. The output of scalable approaches based on a non-compartmentalized modeling may be so large that it has neither been explored nor handled so far. RESULTS: We present the Miscoto tool to facilitate the selection of a community optimizing a desired function in a microbiome by reporting several possibilities which can be then sorted according to biological criteria. Communities are exhaustively identified using logical programming and by combining the non-compartmentalized and the compartmentalized frameworks. The benchmarking of 4.9 million metabolic functions associated with the Human Microbiome Project, shows that Miscoto is suited to screen and classify metabolic producibility in terms of feasibility, functional redundancy and cooperation processes involved. As an illustration of a host-microbial system, screening the Recon 2.2 human metabolism highlights the role of different consortia within a family of 773 intestinal bacteria. AVAILABILITY AND IMPLEMENTATION: Miscoto source code, instructions for use and examples are available at: https://github.com/cfrioux/miscoto. Oxford University Press 2018-09-01 2018-09-08 /pmc/articles/PMC6129287/ /pubmed/30423063 http://dx.doi.org/10.1093/bioinformatics/bty588 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Eccb 2018: European Conference on Computational Biology Proceedings Frioux, Clémence Fremy, Enora Trottier, Camille Siegel, Anne Scalable and exhaustive screening of metabolic functions carried out by microbial consortia |
title | Scalable and exhaustive screening of metabolic functions carried out by microbial consortia |
title_full | Scalable and exhaustive screening of metabolic functions carried out by microbial consortia |
title_fullStr | Scalable and exhaustive screening of metabolic functions carried out by microbial consortia |
title_full_unstemmed | Scalable and exhaustive screening of metabolic functions carried out by microbial consortia |
title_short | Scalable and exhaustive screening of metabolic functions carried out by microbial consortia |
title_sort | scalable and exhaustive screening of metabolic functions carried out by microbial consortia |
topic | Eccb 2018: European Conference on Computational Biology Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6129287/ https://www.ncbi.nlm.nih.gov/pubmed/30423063 http://dx.doi.org/10.1093/bioinformatics/bty588 |
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