Reconciliation between operational taxonomic units and species boundaries: DynamiC OTU cut-off

Research output: Contribution to journalArticle

Authors

Institutes & Expert groups

  • UGent - Universiteit Gent
  • KUL - Katholieke Universiteit Leuven

Documents & links

DOI

Abstract

The development of high-throughput sequencing technologies has revolutionised the field of microbial ecology via 16S rRNA gene amplicon sequencing approaches. Clustering those amplicon sequencing reads into operational taxonomic units (OTUs) using a fixed cut-off is a commonly used approach to estimate microbial diversity. A 97% threshold was chosen with the intended purpose that resulting OTUs could be interpreted as a proxy for bacterial species. Our results show that the robustness of such a generalised cut-off is questionable when applied to short amplicons only covering one or two variable regions of the 16S rRNA gene. It will lead to biases in diversity metrics and makes it hard to compare results obtained with amplicons derived with different primer sets. The method introduced within this work takes into account the differential evolutional rates of taxonomic lineages in order to define a dynamic and taxonomic-dependent OTU clustering cut-off score. For a taxonomic family consisting of species showing high evolutionary conservation in the amplified variable regions, the cut-off will be more stringent than 97%. By taking into consideration the amplified variable regions and the taxonomic family when defining this cut-off, such a threshold will lead to more robust results and closer correspondence between OTUs and species. This approach has been implemented in a publicly available software package called DynamiC.

Details

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalFEMS Microbiology Ecology
Volume93
DOIs
Publication statusPublished - 1 Apr 2017

Keywords

  • 16S rRNA amplicon sequencing, microbial biodiversity , next generation sequencing, operational taxonomic units (OTU's), OTU clustering, 16S rRNA metagenomics, dynamiC

ID: 3619177