Dataset Information

RAMI: a tool for identification and characterization of phylogenetic clusters in microbial communities.

ABSTRACT: MOTIVATION: The most common approach to estimate microbial diversity is based on the analysis of DNA sequences of specific target genes including ribosomal genes. Commonly, the sequences are grouped into operational taxonomic units based on genetic distance (sequence similarity) instead of genetic change (patristic distance). This method may fail to adequately identify clusters of evolutionary related sequences and it provides no information on the phylogenetic structure of the community. An ease-of-use web application for this purpose has been missing. RESULTS: We have developed RAMI, which clusters related nodes in a phylogenetic tree based on the patristic distance. RAMI also produces indices of cluster properties and other indices used in population and community studies on-the-fly. AVAILABILITY: RAMI is licensed under GNU GPL and can be run or downloaded from http://www.acgt.se/online.html. SUPPLEMENTARY INFORMATION: http://www.acgt.se/RAMI/SuppInfo.

SUBMITTER: Pommier T

PROVIDER: S-EPMC2654800 | biostudies-other | 2009 Mar

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

RAMI: a tool for identification and characterization of phylogenetic clusters in microbial communities.

Pommier Thomas T Canbäck Björn B Lundberg Per P Hagström Ake A Tunlid Anders A

Bioinformatics (Oxford, England) 20090217 6

<h4>Motivation</h4>The most common approach to estimate microbial diversity is based on the analysis of DNA sequences of specific target genes including ribosomal genes. Commonly, the sequences are grouped into operational taxonomic units based on genetic distance (sequence similarity) instead of genetic change (patristic distance). This method may fail to adequately identify clusters of evolutionary related sequences and it provides no information on the phylogenetic structure of the community. ...[more]

PMID: 19223450

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.

Project description:BACKGROUND:The 16S rRNA gene-based amplicon sequencing analysis is widely used to determine the taxonomic composition of microbial communities. Once the taxonomic composition of each community is obtained, evolutionary relationships among taxa are inferred by a phylogenetic tree. Thus, the combined representation of taxonomic composition and phylogenetic relationships among taxa is a powerful method for understanding microbial community structure; however, applying phylogenetic tree-based representation with information on the abundance of thousands or more taxa in each community is a difficult task. For this purpose, we previously developed the tool VITCOMIC (VIsualization tool for Taxonomic COmpositions of MIcrobial Community), which is based on the genome-sequenced microbes' phylogenetic information. Here, we introduce VITCOMIC2, which incorporates substantive improvements over VITCOMIC that were necessary to address several issues associated with 16S rRNA gene-based analysis of microbial communities. RESULTS:We developed VITCOMIC2 to provide (i) sequence identity searches against broad reference taxa including uncultured taxa; (ii) normalization of 16S rRNA gene copy number differences among taxa; (iii) rapid sequence identity searches by applying the graphics processing unit-based sequence identity search tool CLAST; (iv) accurate taxonomic composition inference and nearly full-length 16S rRNA gene sequence reconstructions for metagenomic shotgun sequencing; and (v) an interactive user interface for simultaneous representation of the taxonomic composition of microbial communities and phylogenetic relationships among taxa. We validated the accuracy of processes (ii) and (iv) by using metagenomic shotgun sequencing data from a mock microbial community. CONCLUSIONS:The improvements incorporated into VITCOMIC2 enable users to acquire an intuitive understanding of microbial community composition based on the 16S rRNA gene sequence data obtained from both metagenomic shotgun and amplicon sequencing.

Dataset Information

RAMI: a tool for identification and characterization of phylogenetic clusters in microbial communities.

Publications

RAMI: a tool for identification and characterization of phylogenetic clusters in microbial communities.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure