Ananke: temporal clustering reveals ecological dynamics of microbial communities

PeerJ

article

Michael W. Hall¹  Robin R. Rohwer²  Jonathan Perrie³  Katherine D. McMahon⁴  Robert G. Beiko³ 

[1] Faculty of Graduate Studies, Dalhousie University;Environmental Chemistry and Technology Program, University of Wisconsin-Madison;Faculty of Computer Science, Dalhousie University;Department of Civil and Environmental Engineering, University of Wisconsin-Madison;Department of Bacteriology, University of Wisconsin-Madison

关键词: Time series;    Microbiota;    Clustering;    Marker gene;    Visualization;   

DOI  :  10.7717/peerj.3812

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY