Dataset Information

Measures of phylogenetic differentiation provide robust and complementary insights into microbial communities.

ABSTRACT: High-throughput sequencing techniques have made large-scale spatial and temporal surveys of microbial communities routine. Gaining insight into microbial diversity requires methods for effectively analyzing and visualizing these extensive data sets. Phylogenetic ?-diversity measures address this challenge by allowing the relationship between large numbers of environmental samples to be explored using standard multivariate analysis techniques. Despite the success and widespread use of phylogenetic ?-diversity measures, an extensive comparative analysis of these measures has not been performed. Here, we compare 39 measures of phylogenetic ? diversity in order to establish the relative similarity of these measures along with key properties and performance characteristics. While many measures are highly correlated, those commonly used within microbial ecology were found to be distinct from those popular within classical ecology, and from the recently recommended Gower and Canberra measures. Many of the measures are surprisingly robust to different rootings of the gene tree, the choice of similarity threshold used to define operational taxonomic units, and the presence of outlying basal lineages. Measures differ considerably in their sensitivity to rare organisms, and the effectiveness of measures can vary substantially under alternative models of differentiation. Consequently, the depth of sequencing required to reveal underlying patterns of relationships between environmental samples depends on the selected measure. Our results demonstrate that using complementary measures of phylogenetic ? diversity can further our understanding of how communities are phylogenetically differentiated. Open-source software implementing the phylogenetic ?-diversity measures evaluated in this manuscript is available at http://kiwi.cs.dal.ca/Software/ExpressBetaDiversity.

SUBMITTER: Parks DH

PROVIDER: S-EPMC3526167 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Measures of phylogenetic differentiation provide robust and complementary insights into microbial communities.

Parks Donovan H DH Beiko Robert G RG

The ISME journal 20120802 1

High-throughput sequencing techniques have made large-scale spatial and temporal surveys of microbial communities routine. Gaining insight into microbial diversity requires methods for effectively analyzing and visualizing these extensive data sets. Phylogenetic β-diversity measures address this challenge by allowing the relationship between large numbers of environmental samples to be explored using standard multivariate analysis techniques. Despite the success and widespread use of phylogeneti ...[more]

PMID: 22855211

Similar Datasets

Project description:UNLABELLED: Understanding the interactions among different species and their responses to environmental changes, such as elevated atmospheric concentrations of CO(2), is a central goal in ecology but is poorly understood in microbial ecology. Here we describe a novel random matrix theory (RMT)-based conceptual framework to discern phylogenetic molecular ecological networks using metagenomic sequencing data of 16S rRNA genes from grassland soil microbial communities, which were sampled from a long-term free-air CO(2) enrichment experimental facility at the Cedar Creek Ecosystem Science Reserve in Minnesota. Our experimental results demonstrated that an RMT-based network approach is very useful in delineating phylogenetic molecular ecological networks of microbial communities based on high-throughput metagenomic sequencing data. The structure of the identified networks under ambient and elevated CO(2) levels was substantially different in terms of overall network topology, network composition, node overlap, module preservation, module-based higher-order organization, topological roles of individual nodes, and network hubs, suggesting that the network interactions among different phylogenetic groups/populations were markedly changed. Also, the changes in network structure were significantly correlated with soil carbon and nitrogen contents, indicating the potential importance of network interactions in ecosystem functioning. In addition, based on network topology, microbial populations potentially most important to community structure and ecosystem functioning can be discerned. The novel approach described in this study is important not only for research on biodiversity, microbial ecology, and systems microbiology but also for microbial community studies in human health, global change, and environmental management. IMPORTANCE: The interactions among different microbial populations in a community play critical roles in determining ecosystem functioning, but very little is known about the network interactions in a microbial community, owing to the lack of appropriate experimental data and computational analytic tools. High-throughput metagenomic technologies can rapidly produce a massive amount of data, but one of the greatest difficulties is deciding how to extract, analyze, synthesize, and transform such a vast amount of information into biological knowledge. This study provides a novel conceptual framework to identify microbial interactions and key populations based on high-throughput metagenomic sequencing data. This study is among the first to document that the network interactions among different phylogenetic populations in soil microbial communities were substantially changed by a global change such as an elevated CO(2) level. The framework developed will allow microbiologists to address research questions which could not be approached previously, and hence, it could represent a new direction in microbial ecology research.

Dataset Information

Measures of phylogenetic differentiation provide robust and complementary insights into microbial communities.

Publications

Measures of phylogenetic differentiation provide robust and complementary insights into microbial communities.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets