The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
PCR Biases Distort Bacterial and Archaeal Community Structure in Pyrosequencing Datasets
Open accessAbstract
As 16S rRNA gene targeted massively parallel sequencing has become a common tool for microbial diversity investigations, numerous advances have been made to minimize the influence of sequencing and chimeric PCR artifacts through rigorous quality control measures. However, there has been little effort towards understanding the effect of multi-template PCR biases on microbial community structure. In this study, we used three bacterial and three archaeal mock communities consisting of, respectively, 33 bacterial and 24 archaeal 16S rRNA gene sequences combined in different proportions to compare the influences of (1) sequencing depth, (2) sequencing artifacts (sequencing errors and chimeric PCR artifacts), and (3) biases in multi-template PCR, towards the interpretation of community structure in pyrosequencing datasets. We also assessed the influence of each of these three variables on α- and β-diversity metrics that rely on the number of OTUs alone (richness) and those that include both membership and the relative abundance of detected OTUs (diversity). As part of this study, we redesigned bacterial and archaeal primer sets that target the V3-V5 region of the 16S rRNA gene, along with multiplexing barcodes, to permit simultaneous sequencing of PCR products from the two domains. We conclude that the benefits of deeper sequencing efforts extend beyond greater OTU detection and result in higher precision in β-diversity analyses by reducing the variability between replicate libraries, despite the presence of more sequencing artifacts. Additionally, spurious OTUs resulting from sequencing errors have a significant impact on richness or shared-richness based α- and β-diversity metrics, whereas metrics that utilize community structure (including both richness and relative abundance of OTUs) are minimally affected by spurious OTUs. However, the greatest obstacle towards accurately evaluating community structure are the errors in estimated mean relative abundance of each detected OTU due to biases associated with multi-template PCR reactions.
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Cites
Sequencing technologies – the next generation
Metzker, M. L. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2626 · Cited by 7,066
Reducing the Effects of PCR Amplification and Sequencing Artifacts on 16S rRNA-Based Studies
Schloss, P. D., Gevers, D., & Westcott, S. L. (2011) · PLoS ONE · doi:10.1371/journal.pone.0027310 · Cited by 2,112
Ironing out the wrinkles in the rare biosphere through improved OTU clustering
Huse, S. M., Welch, D. M., Morrison, H. G., & Sogin, M. L. (2010) · Environmental Microbiology · doi:10.1111/j.1462-2920.2010.02193.x · Cited by 1,346
Oral microbiome profiles: 16S rRNA pyrosequencing and microarray assay comparison
Ahn, J., Yang, L., Paster, B. J., Ganly, I., Morris, L., Pei, Z., & Hayes, R. B. (2011) · PLoS ONE · doi:10.1371/journal.pone.0022788 · Cited by 183
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Zhang, X., Zhao, Y., Xu, J., Xue, Z., Zhang, M., Pang, X., Zhang, X., & Zhao, L. (2015) · Scientific Reports · doi:10.1038/srep14405 · Cited by 630
The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies
Vaginal Microbiome Consortium (additional members), Brooks, J. P., Edwards, D. J., Harwich, M. D., Rivera, M. C., Fettweis, J. M., Serrano, M. G., Reris, R. A., Sheth, N. U., Huang, B., et al. (2015) · BMC Microbiology · doi:10.1186/s12866-015-0351-6 · Cited by 528
Piñol, J., Mir, G., Gomez‐Polo, P., & Agustí, N. (2015) · Molecular Ecology Resources · doi:10.1111/1755-0998.12355 · Cited by 423
D’Amore, R., Ijaz, U. Z., Schirmer, M., Kenny, J. G., Gregory, R., Darby, A. C., Shakya, M., Podar, M., Quince, C., & Hall, N. (2016) · BMC Genomics · doi:10.1186/s12864-015-2194-9 · Cited by 405
The microbiome in respiratory medicine: current challenges and future perspectives
Faner, R., Sibila, O., Agustí, A., Bernasconi, E., Chalmers, J. D., Huffnagle, G. B., Manichanh, C., Molyneaux, P. L., Paredes, R., Pérez Brocal, V., et al. (2017) · European Respiratory Journal · doi:10.1183/13993003.02086-2016 · Cited by 247
Silage review: Using molecular approaches to define the microbial ecology of silage
McAllister, T. A., Dunière, L., Drouin, P., Xu, S., Wang, Y., Munns, K., & Zaheer, R. (2018) · Journal of Dairy Science · doi:10.3168/jds.2017-13704 · Cited by 162
A combined meta-barcoding and shotgun metagenomic analysis of spontaneous wine fermentation
Sternes, P. R., Lee, D., Kutyna, D. R., & Borneman, A. R. (2017) · GigaScience · doi:10.1093/gigascience/gix040 · Cited by 91
Parente, E., Cocolin, L., De Filippis, F., Zotta, T., Ferrocino, I., O'Sullivan, O., Neviani, E., De Angelis, M., Cotter, P. D., & Ercolini, D. (2016) · International Journal of Food Microbiology · doi:10.1016/j.ijfoodmicro.2015.12.001 · Cited by 82
Romani Vestman, N., Chen, T., Lif Holgerson, P., Öhman, C., & Johansson, I. (2015) · PLoS ONE · doi:10.1371/journal.pone.0125812 · Cited by 78
Gut microbial changes of patients with psychotic and affective disorders: A systematic review
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Metadata
- Authors
- Pinto, A. J., & Raskin, L.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-08-15
- Volume
- 7
- Issue
- 8
- Pages
- e43093
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2087544933
- Cited by
- 443
- References
- 4
- FWCI
- 34.50
- Type
- journal article
- Language
- en
- Open access
- gold
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