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Ananke: temporal clustering reveals ecological dynamics of microbial communities

Taxonomic markers such as the 16S ribosomal RNA gene are widely used in microbial community analysis. A common first step in marker-gene analysis is grouping genes into clusters to reduce data sets to a more manageable size and potentially mitigate the effects of sequencing error. Instead of cluster...

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Autores principales: Hall, Michael W., Rohwer, Robin R., Perrie, Jonathan, McMahon, Katherine D., Beiko, Robert G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5621509/
https://www.ncbi.nlm.nih.gov/pubmed/28966891
http://dx.doi.org/10.7717/peerj.3812
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author Hall, Michael W.
Rohwer, Robin R.
Perrie, Jonathan
McMahon, Katherine D.
Beiko, Robert G.
author_facet Hall, Michael W.
Rohwer, Robin R.
Perrie, Jonathan
McMahon, Katherine D.
Beiko, Robert G.
author_sort Hall, Michael W.
collection PubMed
description Taxonomic markers such as the 16S ribosomal RNA gene are widely used in microbial community analysis. A common first step in marker-gene analysis is grouping genes into clusters to reduce data sets to a more manageable size and potentially mitigate the effects of sequencing error. Instead of clustering based on sequence identity, marker-gene data sets collected over time can be clustered based on temporal correlation to reveal ecologically meaningful associations. We present Ananke, a free and open-source algorithm and software package that complements existing sequence-identity-based clustering approaches by clustering marker-gene data based on time-series profiles and provides interactive visualization of clusters, including highlighting of internal OTU inconsistencies. Ananke is able to cluster distinct temporal patterns from simulations of multiple ecological patterns, such as periodic seasonal dynamics and organism appearances/disappearances. We apply our algorithm to two longitudinal marker gene data sets: faecal communities from the human gut of an individual sampled over one year, and communities from a freshwater lake sampled over eleven years. Within the gut, the segregation of the bacterial community around a food-poisoning event was immediately clear. In the freshwater lake, we found that high sequence identity between marker genes does not guarantee similar temporal dynamics, and Ananke time-series clusters revealed patterns obscured by clustering based on sequence identity or taxonomy. Ananke is free and open-source software available at https://github.com/beiko-lab/ananke.
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spelling pubmed-56215092017-09-29 Ananke: temporal clustering reveals ecological dynamics of microbial communities Hall, Michael W. Rohwer, Robin R. Perrie, Jonathan McMahon, Katherine D. Beiko, Robert G. PeerJ Bioinformatics Taxonomic markers such as the 16S ribosomal RNA gene are widely used in microbial community analysis. A common first step in marker-gene analysis is grouping genes into clusters to reduce data sets to a more manageable size and potentially mitigate the effects of sequencing error. Instead of clustering based on sequence identity, marker-gene data sets collected over time can be clustered based on temporal correlation to reveal ecologically meaningful associations. We present Ananke, a free and open-source algorithm and software package that complements existing sequence-identity-based clustering approaches by clustering marker-gene data based on time-series profiles and provides interactive visualization of clusters, including highlighting of internal OTU inconsistencies. Ananke is able to cluster distinct temporal patterns from simulations of multiple ecological patterns, such as periodic seasonal dynamics and organism appearances/disappearances. We apply our algorithm to two longitudinal marker gene data sets: faecal communities from the human gut of an individual sampled over one year, and communities from a freshwater lake sampled over eleven years. Within the gut, the segregation of the bacterial community around a food-poisoning event was immediately clear. In the freshwater lake, we found that high sequence identity between marker genes does not guarantee similar temporal dynamics, and Ananke time-series clusters revealed patterns obscured by clustering based on sequence identity or taxonomy. Ananke is free and open-source software available at https://github.com/beiko-lab/ananke. PeerJ Inc. 2017-09-26 /pmc/articles/PMC5621509/ /pubmed/28966891 http://dx.doi.org/10.7717/peerj.3812 Text en ©2017 Hall et al. 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
Hall, Michael W.
Rohwer, Robin R.
Perrie, Jonathan
McMahon, Katherine D.
Beiko, Robert G.
Ananke: temporal clustering reveals ecological dynamics of microbial communities
title Ananke: temporal clustering reveals ecological dynamics of microbial communities
title_full Ananke: temporal clustering reveals ecological dynamics of microbial communities
title_fullStr Ananke: temporal clustering reveals ecological dynamics of microbial communities
title_full_unstemmed Ananke: temporal clustering reveals ecological dynamics of microbial communities
title_short Ananke: temporal clustering reveals ecological dynamics of microbial communities
title_sort ananke: temporal clustering reveals ecological dynamics of microbial communities
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5621509/
https://www.ncbi.nlm.nih.gov/pubmed/28966891
http://dx.doi.org/10.7717/peerj.3812
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