Wrinkles in the rare biosphere: pyrosequencing errors can lead to artificial inflation of diversity estimates.

By Victor Kunin, A. Engelbrektson, Howard Ochman, Philip Hugenholtz. Environmental Microbiology, 2009

1,285 citations recorded by the source corpus.

Subfields: Ecology

View Paper: https://www.semanticscholar.org/paper/f38887b7e0dc8d7bc90f870796457210b434fd58

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Citations in the corpus

Soil bacterial and fungal communities across a pH gradient in an arable soil (2010, neutral): “Community level diversity across the pH gradient was analyzed by comparing the number of operational taxonomic units (OTUs) with an OTU defined at the 97% similarity level for both bacteria and fungi (Kunin et al., 2010).”
Swarm v2: highly-scalable and high-resolution amplicon clustering (2015, background): “The fastidious option can be viewed as a way to reduce data loss, as many researchers conservatively consider low abundant OTUs as spurious errors and remove them from downstream analyses (Behnke et al., 2011; Kunin et al., 2010).”
Ultrafast clustering algorithms for metagenomic sequence analysis (2012, result): “In the meantime, many other studies [52–56] found that the single biggest cause of the overestimation problem is the sequence errors or noise, so new methods such as SLP [52], PyroNoise [54], Denoiser [55] and Ampliconnoise [56] focus at identifying and removing sequence noise.”
Ironing out the wrinkles in the rare biosphere through improved OTU clustering (2010, methodology): “Prior to calculating the sequencing error rate, we eliminated the 11–19% of all reads that did not pass previously described quality filtering (Huse et al., 2007; Kunin et al., 2010).”
Spatial patterns of bacterial richness and evenness in the NW Mediterranean Sea explored by pyrosequencing of the 16S rRNA (2010, methodology): “The approach still requires careful evaluation of possible biases and potential errors (Huse et al. 2007, 2008, 2010, Quince et al. 2009, Reeder & Knight 2009, Kunin et al. 2010), as well as studies in more ecosystems, but it has already started a whole new era in the study of microbial diversity.”
Metagenomic Analyses: Past and Future Trends (2010, background): “correction by assembly and sequencing depth, which is typically applied during genome projects, is not feasible (51, 64).”
Defining the Human Microbiome (2012, background): “Both sequencing error[38, 39] and issues related to alignment, especially multiple sequence alignment[40-43], can inflate the number of OTUs immensely.”
Comparison of the Levels of Bacterial Diversity in Freshwater, Intertidal Wetland, and Marine Sediments by Using Millions of Illumina Tags (2012, background): “We set relatively stringent quality controls, because even though the total number of errors were low, they contributed a large number of rare species and led to overestimation of alpha-diversity, as most of these error sequences were singletons or doubletons (12, 16).”
Reshaping gut bacterial communities after dietary Tenebrio molitor larvae meal supplementation in three fish species (2019, positive): “…al. 2012, Estruch et al. 2015, Borsodi et al. 2017) were aligned in the SILVA 126 database (Pruesse et al., 2007), binned into operational taxonomic units (OTUs) and clustered based on averageneighbour algorithm at 97% the sequence similarity cut-off (Kunin et al. 2010, Stackebrandt& Goebel 1994).”
The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome (2012, neutral): “topic [21-23], but certain poorly characterized environ-”
Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. (2011, background): “Often the appearance of ‘‘novel’’ sequences in multiple independent 16S libraries is viewed as confirmation of the validity of such sequences (Kunin et al. 2010), but our results cast doubt on this practice.”
Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample (2010, methodology): “The sequencing of defined communities has been critical for quantifying sequencing error profiles for pyrosequencing (27, 28), but each new method requires validation through the sequencing of “control” samples in addition to the more comprehensive analysis of simulated datasets.”
Reducing the Effects of PCR Amplification and Sequencing Artifacts on 16S rRNA-Based Studies (2011, methodology): “Kunin and colleagues [14] sequenced the 16S rRNA gene from E.”