Dataset Information

Defining DNA-based operational taxonomic units for microbial-eukaryote ecology.

ABSTRACT: DNA sequence information has increasingly been used in ecological research on microbial eukaryotes. Sequence-based approaches have included studies of the total diversity of selected ecosystems, studies of the autecology of ecologically relevant species, and identification and enumeration of species of interest for human health. It is still uncommon, however, to delineate protistan species based on their genetic signatures. The reluctance to assign species-level designations based on DNA sequences is in part a consequence of the limited amount of sequence information presently available for many free-living microbial eukaryotes and in part a consequence of the problematic nature of and debate surrounding the microbial species concept. Despite the difficulties inherent in assigning species names to DNA sequences, there is a growing need to attach meaning to the burgeoning amount of sequence information entering the literature, and there is a growing desire to apply this information in ecological studies. We describe a computer-based tool that assigns DNA sequences from environmental databases to operational taxonomic units at approximately species-level distinctions. This approach provides a practical method for ecological studies of microbial eukaryotes (primarily protists) by enabling semiautomated analysis of large numbers of samples spanning great taxonomic breadth. Derivation of the algorithm was based on an analysis of complete small-subunit (18S) rRNA gene sequences and partial gene sequences obtained from the GenBank database for morphologically described protistan species. The program was tested using environmental 18S rRNA data sets for two oceanic ecosystems. A total of 388 operational taxonomic units were observed for 2,207 sequences obtained from samples collected in the western North Atlantic and eastern North Pacific oceans.

SUBMITTER: Caron DA

PROVIDER: S-EPMC2747860 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature

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Defining DNA-based operational taxonomic units for microbial-eukaryote ecology.

Caron David A DA Countway Peter D PD Savai Pratik P Gast Rebecca J RJ Schnetzer Astrid A Moorthi Stefanie D SD Dennett Mark R MR Moran Dawn M DM Jones Adriane C AC

Applied and environmental microbiology 20090710 18

DNA sequence information has increasingly been used in ecological research on microbial eukaryotes. Sequence-based approaches have included studies of the total diversity of selected ecosystems, studies of the autecology of ecologically relevant species, and identification and enumeration of species of interest for human health. It is still uncommon, however, to delineate protistan species based on their genetic signatures. The reluctance to assign species-level designations based on DNA sequenc ...[more]

PMID: 19592529

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Project description:Assignment of 16S rRNA gene sequences to operational taxonomic units (OTUs) is a computational bottleneck in the process of analyzing microbial communities. Although this has been an active area of research, it has been difficult to overcome the time and memory demands while improving the quality of the OTU assignments. Here, we developed a new OTU assignment algorithm that iteratively reassigns sequences to new OTUs to optimize the Matthews correlation coefficient (MCC), a measure of the quality of OTU assignments. To assess the new algorithm, OptiClust, we compared it to 10 other algorithms using 16S rRNA gene sequences from two simulated and four natural communities. Using the OptiClust algorithm, the MCC values averaged 15.2 and 16.5% higher than the OTUs generated when we used the average neighbor and distance-based greedy clustering with VSEARCH, respectively. Furthermore, on average, OptiClust was 94.6 times faster than the average neighbor algorithm and just as fast as distance-based greedy clustering with VSEARCH. An empirical analysis of the efficiency of the algorithms showed that the time and memory required to perform the algorithm scaled quadratically with the number of unique sequences in the data set. The significant improvement in the quality of the OTU assignments over previously existing methods will significantly enhance downstream analysis by limiting the splitting of similar sequences into separate OTUs and merging of dissimilar sequences into the same OTU. The development of the OptiClust algorithm represents a significant advance that is likely to have numerous other applications. IMPORTANCE The analysis of microbial communities from diverse environments using 16S rRNA gene sequencing has expanded our knowledge of the biogeography of microorganisms. An important step in this analysis is the assignment of sequences into taxonomic groups based on their similarity to sequences in a database or based on their similarity to each other, irrespective of a database. In this study, we present a new algorithm for the latter approach. The algorithm, OptiClust, seeks to optimize a metric of assignment quality by shuffling sequences between taxonomic groups. We found that OptiClust produces more robust assignments and does so in a rapid and memory-efficient manner. This advance will allow for a more robust analysis of microbial communities and the factors that shape them.

Dataset Information

Defining DNA-based operational taxonomic units for microbial-eukaryote ecology.

Publications

Defining DNA-based operational taxonomic units for microbial-eukaryote ecology.

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