Reducing the Effects of PCR Amplification and Sequencing Artifacts on 16S rRNA-Based Studies

By Patrick D. Schloss, Dirk Gevers, Sarah L. Westcott. PLoS ONE, 2011

2,075 citations recorded by the source corpus.

The advent of next generation sequencing has coincided with a growth in interest in using these approaches to better understand the role of the structure and function of the microbial communities in human, animal, and environmental health. Yet, use of next generation sequencing to perform 16S rRNA gene sequence surveys has resulted in considerable controversy surrounding the effects of sequencing errors on downstream analyses. We analyzed 2.7×106 reads distributed among 90 identical mock community samples, which were collections of genomic DNA from 21 different species with known 16S rRNA gene sequences; we observed an average error rate of 0.0060. To improve this error rate, we evaluated numerous methods of identifying bad sequence reads, identifying regions within reads of poor quality, and correcting base calls and were able to reduce the overall error rate to 0.0002. Implementation of the PyroNoise algorithm provided the best combination of error rate, sequence length, and number of sequences. Perhaps more problematic than sequencing errors was the presence of chimeras generated during PCR. Because we knew the true sequences within the mock community and the chimeras they could form, we identified 8% of the raw sequence reads as chimeric. After quality filtering the raw sequences and using the Uchime chimera detection program, the overall chimera rate decreased to 1%. The chimeras that could not be detected were largely responsible for the identification of spurious operational taxonomic units (OTUs) and genus-level phylotypes. The number of spurious OTUs and phylotypes increased with sequencing effort indicating that comparison of communities should be made using an equal number of sequences. Finally, we applied our improved quality-filtering pipeline to several benchmarking studies and observed that even with our stringent data curation pipeline, biases in the data generation pipeline and batch effects were observed that could potentially confound the interpretation of microbial community data.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e94f9365ae6ab514b6289f596c805a28b8f79298

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Citations in the corpus

Ecology and exploration of the rare biosphere (2015, background): “In a study of multiple processing pipelines, Schloss et al. 169 observed that pyrosequencing reads had an estimated error rate of 0.006 (number of errors per total base calls) in a mock community.”
Intestinal Blautia is associated with reduced death from graft-versus-host disease (2015, methodology): “34 [26], screened and filtered for quality [27] then classified to the species level [28] using a modified form of the Greengenes reference database [29].”
Intestinal domination and the risk of bacteremia in patients undergoing allogeneic hematopoietic stem cell transplantation. (2012, methodology): “Sequence data were screened and filtered for quality [17], then aligned to the full-length 16S rRNA gene, using as template the SILVA reference alignment [18].”
Plant growth-promoting rhizobacteria enhance the growth and Cd uptake of Sedum plumbizincicola in a Cd-contaminated soil (2015, methodology): “2.8 Pyrosequencing data processing The 16S rRNA gene sequence data were analyzed by the pyrosequencing pipeline tools available from the Ribosomal Database Project (RDP) and MOTHUR version 1.24.1 (Schloss et al. 2011).”
The ecology of environmental DNA and implications for conservation genetics (2016, methodology): “2009) and positive controls with mock community DNA (Schloss et al. 2011).”
Error filtering, pair assembly and error correction for next-generation sequencing reads (2015, background): “Other articles describing analysis pipelines for amplicon reads (e.g. Kozich et al. , 2013; Schloss et al. , 2011) report the mean error rate but do not consider the tail of the error distribution, which we believe to be important due to the spurious clusters and consequent inflated estimates of…”
obitools: a unix‐inspired software package for DNA metabarcoding (2016, methodology): “Sequencing of PCR amplicons using NGS typically reveals a lot of variants for each sequence present in the ampliﬁed mix, and these correspond mainly to PCR errors (Schloss et al. 2011; Coissac et al. 2012).”
Reagent and laboratory contamination can critically impact sequence-based microbiome analyses (2014, methodology): “For the nasopharyngeal microbiota study, the data were processed, cleaned and analysed using the mothur Schloss SOP [73] and randomly subsampled to 200 sequence reads per sample.”
Long-term changes in soil microbial communities during primary succession (2014, methodology): “200 201 Bacterial 16S rRNA sequence analysis was carried out in mothur 1.31.1 (Schloss et al., 202 2009) following the recommendations of Schloss et al. (Schloss et al., 2011).”