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Defining DNA-based operational taxonomic units for microbial-eukaryote ecology
David A Caron1, Peter D Countway, Pratik Savai
1Department of Biological Sciences, University of Southern California, 3616 Trousdale Parkway, Los Angeles, CA 90089-0371, USA. dcaron@usc.edu
Applied and Environmental Microbiology
|July 14, 2009
Summary
A new computer tool aids ecological studies by assigning DNA sequences to microbial eukaryote species. This method enables practical, semiautomated analysis of vast microbial diversity in environmental samples.
Area of Science:
- Microbial Ecology
- Molecular Biology
- Bioinformatics
Background:
- DNA sequencing is vital for microbial eukaryote ecology, aiding ecosystem diversity studies and identifying human health-relevant species.
- Delineating protistan species by genetic signatures remains uncommon due to limited sequence data and debates on the microbial species concept.
Purpose of the Study:
- To develop a computer-based tool for assigning DNA sequences to operational taxonomic units (OTUs) at approximately species-level distinctions.
- To provide a practical method for ecological studies of microbial eukaryotes, enabling semiautomated analysis of large sample sets.
Main Methods:
- The algorithm was derived from analyzing complete small-subunit (18S) rRNA gene sequences and partial gene sequences from GenBank.
- Morphologically described protistan species were used as a reference for developing the species-level distinction criteria.
- The tool was tested using environmental 18S rRNA datasets from two oceanic ecosystems.
Main Results:
- The computer tool successfully assigns environmental DNA sequences to OTUs at approximately species-level distinctions.
- Analysis of oceanic datasets identified 388 OTUs from 2,207 sequences collected in the western North Atlantic and eastern North Pacific.
Conclusions:
- This computational approach offers a practical solution for the ecological study of microbial eukaryotes, particularly protists.
- It facilitates the semiautomated analysis of extensive sequence data, advancing our understanding of microbial biodiversity across broad taxonomic groups.
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