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PeerJ. 2017 Sep 26;5:e3812. doi: 10.7717/peerj.3812. eCollection 2017.

Ananke: temporal clustering reveals ecological dynamics of microbial communities.

Author information

1
Faculty of Graduate Studies, Dalhousie University, Halifax, Nova Scotia, Canada.
2
Environmental Chemistry and Technology Program, University of Wisconsin-Madison, Madison, WI, United States of America.
3
Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada.
4
Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI, United States of America.
5
Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, United States of America.

Abstract

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.

KEYWORDS:

Clustering; Marker gene; Microbiota; Time series; Visualization

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