Accurate determination of microbial diversity from 454 pyrosequencing data

By Christopher Quince, Anders Lanzén, Thomas P. Curtis, Russell J. Davenport, N. Hall, I. Head, L. F. Read, William T. Sloan. Nature Methods, 2009

1,052 citations recorded by the source corpus.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f05765b94f8a1dcff4291d9ac6a1bd16b295cb1a

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Citations in the corpus

Biodiversity soup: metabarcoding of arthropods for rapid biodiversity assessment and biomonitoring (2012, background): “…far (Ratnasingham&Hebert 2007), and finally, the mutational properties of COI offer the opportunity to eliminate most pyrosequencing error (Emerson et al. 2011; Ranwez et al. 2011), a phenomenon that, if uncorrected, results in overestimates of diversity (Quince et al. 2009; Reeder &Knight 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.”
Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates (2017, background): “…homogenizing effect on the widely different initial clustering results, in the sense that all the methods resulted in relatively good predictions of α -diversity and much more similar and improved results for the other diversity metrics after curation with LULU (Table 1, Supplementary Figs 1 – 7).”
Comparison of the Levels of Bacterial Diversity in Freshwater, Intertidal Wetland, and Marine Sediments by Using Millions of Illumina Tags (2012, background): “Shortly thereafter, it was realized that sequencing errors introduced by the pyrosequencing contributed a large fraction of socalled rare species and that richness estimators were extremely sensitive to the error-prone sequences (25, 27).”
Removing Noise From Pyrosequenced Amplicons (2011, methodology): “PyroNoise is a relatively sophisticated algorithm that reconstructs the true sequences and frequencies in the sample prior to OTU construction using a mixture model [5].”
A first comprehensive census of fungi in soil reveals both hyperdiversity and fine-scale niche partitioning (2014, background): “…may lump discrete sister species (Kindermann et al. 1998, Horton 2002, Geml et al. 2006, 2009); on the other hand, a more rigorous threshold would begin to artificially divide species due to sequencing errors and intraspecific variation (O’Brien et al. 2005, Quince et al. 2009, Kunin et al. 2010).”
Fungal community analysis by high-throughput sequencing of amplified markers – a user's guide (2013, methodology): “The read-score-based base-pair pruning applied by the sequencing factory is at best a poor replacement for sequence quality management programs such as AMPLICONNOISE (Quince et al., 2011), the Denoiser implemented in QIIME (Reeder & Knight, 2010), DADA (Rosen et al., 2012) and ACACIA (Bragg et al.,…”
Shining a Light on Dark Sequencing: Characterising Errors in Ion Torrent PGM Data (2013, background): “Thus, relationships between error occurrence and features of the read and sequencing parameters are best examined directly at the flow-value level [15].”
The Gut Microbiome Modulates Colon Tumorigenesis (2013, background): “450 flows (38), aligned the resulting sequences to a reference alignment”
Associating microbiome composition with environmental covariates using generalized UniFrac distances (2012, methodology): “The first strategy is a modification of the simulation method proposed by Schloss (2008), where we draw points (16S rRNA sequences) from a 2D circle with known densities (Fig.”
A Metagenomic Approach to Characterization of the Vaginal Microbiome Signature in Pregnancy (2012, background): “Rare species, which tend to cause inflated species diversity estimations [30], are not included.”
Rapid denoising of pyrosequencing amplicon data: exploiting the rank-abundance distribution (2010, methodology): “…by clustering the flowgrams (patterns of intensities in each read) before conversion to sequences to eliminate issues due to homopolymer read errors 10 , yet this approach is exceedingly computationally expensive and beyond the reach of most individual investigators who do not have access to…”
Reducing the Effects of PCR Amplification and Sequencing Artifacts on 16S rRNA-Based Studies (2011, methodology): “used as the reference dataset [23].”
DECIPHER, a Search-Based Approach to Chimera Identification for 16S rRNA Sequences (2011, background): “Eliminating chimeric sequences is now understood by the research community as an important step to take before submitting sequences to public databases or before assembling sequences from short fragments produced by next-generation sequencing approaches (8, 10, 13).”
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.”