Assessing and Improving Methods Used in Operational Taxonomic Unit-Based Approaches for 16S rRNA Gene Sequence Analysis

By Patrick D. Schloss, Sarah L. Westcott. Applied and Environmental Microbiology, 2011

684 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/18f626cfbdee7cff149471b8a78ac1cb067ac08d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

In Molecular Biology

Citations in the corpus

Machine learning for metagenomics: methods and tools (2015, methodology): “Moreover, a number of studies point out that different clustering methods provide non-equivalent clusters for the same dataset, quantitatively in terms of total number of clusters and OTU size distributions as well as qualitatively in terms of cluster composition (Huse et al., 2010; Schloss and West”
Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics (2016, background): “In our experience, sacrificing sequences with low quality scores often improves the accuracy of the analyses by a significant margin.”
Standard methods for research on Apis mellifera gut symbionts (2013, background): “No single sequence identity threshold exists for matching bacterial species, as defined by phenotype (Schloss and Westcott, 2011).”
A general species delimitation method with applications to phylogenetic placements (2013, methodology): “, 2012) or taxonomy-dependent methods (Schloss and Westcott, 2011).”
Metabarcoding reveals strong spatial structure and temporal turnover of zooplankton communities among marine and freshwater ports (2016, background): “Another limitation stems from bioinformatics pipelines that implement sequence clustering prior to taxonomic assignments, reducing taxonomic resolution and leading to incorrect assignments particularly at the species level (Schloss & Westcott, 2011; Brown et al., 2015).”
Reducing the Effects of PCR Amplification and Sequencing Artifacts on 16S rRNA-Based Studies (2011, methodology): “Finally, because the 16S rRNA gene does not evolve uniformly over its length and the sequences resulting from this trimming algorithm generated sequences of varying length, it was necessary to further trim the sequences to a region where the average sequence was 200 bp long and all of the sequences ”
Development of a Dual-Index Sequencing Strategy and Curation Pipeline for Analyzing Amplicon Sequence Data on the MiSeq Illumina Sequencing Platform (2013, methodology): “Finally, sequences were split into groups corresponding to their taxonomy at the level of order and then assigned to operational taxonomic units (OTUs) at a 3% dissimilarity level; previous experience indicates that the OTU assignments by this approach are equivalent to not splitting the sequences b”
Updating the 97% identity threshold for 16S ribosomal RNA OTUs (2017, methodology): “Several OTU quality metrics have been proposed, including richness (e.g., (Sun et al., 2009)), normalized mutual information (Cai and Sun, 2011; Zheng et al., 2012) and Matthews’ Correlation Coefficient (Schloss and Westcott, 2011).”