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Robust and automatic definition of microbiome states

Analysis of microbiome dynamics would allow elucidation of patterns within microbial community evolution under a variety of biologically or economically important circumstances; however, this is currently hampered in part by the lack of rigorous, formal, yet generally-applicable approaches to discer...

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Detalles Bibliográficos
Autores principales: García-Jiménez, Beatriz, Wilkinson, Mark D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6440462/
https://www.ncbi.nlm.nih.gov/pubmed/30941274
http://dx.doi.org/10.7717/peerj.6657
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author García-Jiménez, Beatriz
Wilkinson, Mark D.
author_facet García-Jiménez, Beatriz
Wilkinson, Mark D.
author_sort García-Jiménez, Beatriz
collection PubMed
description Analysis of microbiome dynamics would allow elucidation of patterns within microbial community evolution under a variety of biologically or economically important circumstances; however, this is currently hampered in part by the lack of rigorous, formal, yet generally-applicable approaches to discerning distinct configurations of complex microbial populations. Clustering approaches to define microbiome “community state-types” at a population-scale are widely used, though not yet standardized. Similarly, distinct variations within a state-type are well documented, but there is no rigorous approach to discriminating these more subtle variations in community structure. Finally, intra-individual variations with even fewer differences will likely be found in, for example, longitudinal data, and will correlate with important features such as sickness versus health. We propose an automated, generic, objective, domain-independent, and internally-validating procedure to define statistically distinct microbiome states within datasets containing any degree of phylotypic diversity. Robustness of state identification is objectively established by a combination of diverse techniques for stable cluster verification. To demonstrate the efficacy of our approach in detecting discreet states even in datasets containing highly similar bacterial communities, and to demonstrate the broad applicability of our method, we reuse eight distinct longitudinal microbiome datasets from a variety of ecological niches and species. We also demonstrate our algorithm’s flexibility by providing it distinct taxa subsets as clustering input, demonstrating that it operates on filtered or unfiltered data, and at a range of different taxonomic levels. The final output is a set of robustly defined states which can then be used as general biomarkers for a wide variety of downstream purposes such as association with disease, monitoring response to intervention, or identifying optimally performant populations.
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spelling pubmed-64404622019-04-02 Robust and automatic definition of microbiome states García-Jiménez, Beatriz Wilkinson, Mark D. PeerJ Bioinformatics Analysis of microbiome dynamics would allow elucidation of patterns within microbial community evolution under a variety of biologically or economically important circumstances; however, this is currently hampered in part by the lack of rigorous, formal, yet generally-applicable approaches to discerning distinct configurations of complex microbial populations. Clustering approaches to define microbiome “community state-types” at a population-scale are widely used, though not yet standardized. Similarly, distinct variations within a state-type are well documented, but there is no rigorous approach to discriminating these more subtle variations in community structure. Finally, intra-individual variations with even fewer differences will likely be found in, for example, longitudinal data, and will correlate with important features such as sickness versus health. We propose an automated, generic, objective, domain-independent, and internally-validating procedure to define statistically distinct microbiome states within datasets containing any degree of phylotypic diversity. Robustness of state identification is objectively established by a combination of diverse techniques for stable cluster verification. To demonstrate the efficacy of our approach in detecting discreet states even in datasets containing highly similar bacterial communities, and to demonstrate the broad applicability of our method, we reuse eight distinct longitudinal microbiome datasets from a variety of ecological niches and species. We also demonstrate our algorithm’s flexibility by providing it distinct taxa subsets as clustering input, demonstrating that it operates on filtered or unfiltered data, and at a range of different taxonomic levels. The final output is a set of robustly defined states which can then be used as general biomarkers for a wide variety of downstream purposes such as association with disease, monitoring response to intervention, or identifying optimally performant populations. PeerJ Inc. 2019-03-26 /pmc/articles/PMC6440462/ /pubmed/30941274 http://dx.doi.org/10.7717/peerj.6657 Text en ©2019 García-Jiménez and Wilkinson 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 use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
García-Jiménez, Beatriz
Wilkinson, Mark D.
Robust and automatic definition of microbiome states
title Robust and automatic definition of microbiome states
title_full Robust and automatic definition of microbiome states
title_fullStr Robust and automatic definition of microbiome states
title_full_unstemmed Robust and automatic definition of microbiome states
title_short Robust and automatic definition of microbiome states
title_sort robust and automatic definition of microbiome states
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6440462/
https://www.ncbi.nlm.nih.gov/pubmed/30941274
http://dx.doi.org/10.7717/peerj.6657
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