Assessing and improving methods used in operational taxonomic unit-based approaches for 16S rRNA gene sequence analysis

Appl Environ Microbiol. 2011 May;77(10):3219-26. doi: 10.1128/AEM.02810-10. Epub 2011 Mar 18.


In spite of technical advances that have provided increases in orders of magnitude in sequencing coverage, microbial ecologists still grapple with how to interpret the genetic diversity represented by the 16S rRNA gene. Two widely used approaches put sequences into bins based on either their similarity to reference sequences (i.e., phylotyping) or their similarity to other sequences in the community (i.e., operational taxonomic units [OTUs]). In the present study, we investigate three issues related to the interpretation and implementation of OTU-based methods. First, we confirm the conventional wisdom that it is impossible to create an accurate distance-based threshold for defining taxonomic levels and instead advocate for a consensus-based method of classifying OTUs. Second, using a taxonomic-independent approach, we show that the average neighbor clustering algorithm produces more robust OTUs than other hierarchical and heuristic clustering algorithms. Third, we demonstrate several steps to reduce the computational burden of forming OTUs without sacrificing the robustness of the OTU assignment. Finally, by blending these solutions, we propose a new heuristic that has a minimal effect on the robustness of OTUs and significantly reduces the necessary time and memory requirements. The ability to quickly and accurately assign sequences to OTUs and then obtain taxonomic information for those OTUs will greatly improve OTU-based analyses and overcome many of the challenges encountered with phylotype-based methods.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Cluster Analysis
  • Computational Biology / methods*
  • DNA, Bacterial / chemistry
  • DNA, Bacterial / genetics
  • DNA, Ribosomal / chemistry
  • DNA, Ribosomal / genetics
  • Metagenomics / methods*
  • Phylogeny
  • RNA, Ribosomal, 16S / genetics*
  • Sequence Analysis / methods*


  • DNA, Bacterial
  • DNA, Ribosomal
  • RNA, Ribosomal, 16S