OTU accuracy results
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11-Aug-2018 New paper describes octave plots for visualizing alpha diversity.

12-Jun-2018 New paper shows that one in five taxonomy annotations in SILVA and Greengenes are wrong.

18-Apr-2018 New paper shows that taxonomy prediction accuracy is <50% for V4 sequences.

05-Oct-2017 PeerJ paper shows low accuracy of closed- and open-ref. QIIME OTUs.

22-Sep-2017 New paper shows 97% threshold is wrong, OTUs should be 99% full-length 16S, 100% for V4.

UPARSE tutorial video posted on YouTube. Make OTUs from MiSeq reads.



OTU accuracy results

See also
  OTU benchmark


OTUs on mock community with 21 species.
Some species are missing from some samples; in those cases it is reasonable for a method to report < 21 OTUs.

In all 16S mock datasets, a large majority of UPARSE OTUs are Perfect, Good or Contaminant, i.e. are accurate reconstructions of biological sequences. See OTU classification for details of categories Perfect, Good etc.

By contrast, from 41% (Stag3P) to 71% (Even1P) of mothur OTUs were classified as Chimeric. A large fraction of QIIME OTUs was Chimeric, ranging from 23% (Stag3P) to 67% (Even1P). QIIME reported much larger numbers of OTUs than UPARSE or mothur on the pyrosequenced sets, ranging from 1,900 (Stag3P) to 3,647 (Stag2P). With AmpliconNoise, from 15% (Stag1P) to 64% (Even1P) of OTUs were chimeric.

Fungal mock communities
(Not shown in pie charts). On the fungal ITS communities, QIIME again produced large numbers of OTUs, none of which was within 3% of a known biological sequence, while UPARSE successfully reconstructed many of the expected species and many contaminants, though results on these datasets are harder to interpret. See the UPARSE paper for further discussion.

Edgar, R.C. (2013) UPARSE: Highly accurate OTU sequences from microbial amplicon reads, Nature Methods [Pubmed:23955772dx.doi.org/10.1038/nmeth.2604].