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Flynn JM, Brown EA, Chain FJ, et al. Toward accurate molecular identification of species in complex environmental samples: testing the performance of sequence filtering and clustering methods. Ecol Evol. 2015;5(11):2252-66doi: 10.1002/ece3.1497.
Flynn, J. M., Brown, E. A., Chain, F. J., MacIsaac, H. J., & Cristescu, M. E. (2015). Toward accurate molecular identification of species in complex environmental samples: testing the performance of sequence filtering and clustering methods. Ecology and evolution, 5(11), 2252-66. https://doi.org/10.1002/ece3.1497
Flynn, Jullien M, et al. "Toward accurate molecular identification of species in complex environmental samples: testing the performance of sequence filtering and clustering methods." Ecology and evolution vol. 5,11 (2015): 2252-66. doi: https://doi.org/10.1002/ece3.1497
Flynn JM, Brown EA, Chain FJ, MacIsaac HJ, Cristescu ME. Toward accurate molecular identification of species in complex environmental samples: testing the performance of sequence filtering and clustering methods. Ecol Evol. 2015 Jun;5(11):2252-66. doi: 10.1002/ece3.1497. Epub 2015 May 13. PMID: 26078860; PMCID: PMC4461425.
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Ecol Evol. 2015 Jun;5(11):2252-66. doi: 10.1002/ece3.1497. Epub 2015 May 13.

Toward accurate molecular identification of species in complex environmental samples: testing the performance of sequence filtering and clustering methods.

Ecology and evolution

Jullien M Flynn, Emily A Brown, Frédéric J J Chain, Hugh J MacIsaac, Melania E Cristescu

Affiliations

  1. Department of Biology, McGill University 1205 Docteur Penfield, Stewart Biology Building, Montreal, Quebec, Canada, H3A 1B1.
  2. Department of Biology, McGill University 1205 Docteur Penfield, Stewart Biology Building, Montreal, Quebec, Canada, H3A 1B1 ; Great Lakes Institute for Environmental Research, University of Windsor Windsor, Ontario, Canada.
  3. Great Lakes Institute for Environmental Research, University of Windsor Windsor, Ontario, Canada.

PMID: 26078860 PMCID: PMC4461425 DOI: 10.1002/ece3.1497

Abstract

Metabarcoding has the potential to become a rapid, sensitive, and effective approach for identifying species in complex environmental samples. Accurate molecular identification of species depends on the ability to generate operational taxonomic units (OTUs) that correspond to biological species. Due to the sometimes enormous estimates of biodiversity using this method, there is a great need to test the efficacy of data analysis methods used to derive OTUs. Here, we evaluate the performance of various methods for clustering length variable 18S amplicons from complex samples into OTUs using a mock community and a natural community of zooplankton species. We compare analytic procedures consisting of a combination of (1) stringent and relaxed data filtering, (2) singleton sequences included and removed, (3) three commonly used clustering algorithms (mothur, UCLUST, and UPARSE), and (4) three methods of treating alignment gaps when calculating sequence divergence. Depending on the combination of methods used, the number of OTUs varied by nearly two orders of magnitude for the mock community (60-5068 OTUs) and three orders of magnitude for the natural community (22-22191 OTUs). The use of relaxed filtering and the inclusion of singletons greatly inflated OTU numbers without increasing the ability to recover species. Our results also suggest that the method used to treat gaps when calculating sequence divergence can have a great impact on the number of OTUs. Our findings are particularly relevant to studies that cover taxonomically diverse species and employ markers such as rRNA genes in which length variation is extensive.

Keywords: 18S rRNA; OTU; biodiversity; eDNA; high-throughput sequencing; metabarcoding

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