The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance
Open accessAbstract
The abundance of different SSU rRNA ("16S") gene sequences in environmental samples is widely used in studies of microbial ecology as a measure of microbial community structure and diversity. However, the genomic copy number of the 16S gene varies greatly - from one in many species to up to 15 in some bacteria and to hundreds in some microbial eukaryotes. As a result of this variation the relative abundance of 16S genes in environmental samples can be attributed both to variation in the relative abundance of different organisms, and to variation in genomic 16S copy number among those organisms. Despite this fact, many studies assume that the abundance of 16S gene sequences is a surrogate measure of the relative abundance of the organisms containing those sequences. Here we present a method that uses data on sequences and genomic copy number of 16S genes along with phylogenetic placement and ancestral state estimation to estimate organismal abundances from environmental DNA sequence data. We use theory and simulations to demonstrate that 16S genomic copy number can be accurately estimated from the short reads typically obtained from high-throughput environmental sequencing of the 16S gene, and that organismal abundances in microbial communities are more strongly correlated with estimated abundances obtained from our method than with gene abundances. We re-analyze several published empirical data sets and demonstrate that the use of gene abundance versus estimated organismal abundance can lead to different inferences about community diversity and structure and the identity of the dominant taxa in microbial communities. Our approach will allow microbial ecologists to make more accurate inferences about microbial diversity and abundance based on 16S sequence data.
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QIIME allows analysis of high-throughput community sequencing data
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Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB
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Phylogenies and the Comparative Method
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Self-citationBacterial Community Variation in Human Body Habitats Across Space and Time
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The Madness of Microbiome: Attempting To Find Consensus “Best Practice” for 16S Microbiome Studies
Pollock, J., Glendinning, L., Wisedchanwet, T., & Watson, M. (2018) · Applied and Environmental Microbiology · doi:10.1128/aem.02627-17 · Cited by 575
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
Marine Plastic Debris: A New Surface for Microbial Colonization
Wright, R. J., Erni-Cassola, G., Zadjelovic, V., Latva, M., & Christie-Oleza, J. A. (2020) · Environmental Science & Technology · doi:10.1021/acs.est.0c02305 · Cited by 513
Metadata
- Authors
- Kembel, S. W., Wu, M., Eisen, J. A., & Green, J. L.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-10-25
- Volume
- 8
- Issue
- 10
- Pages
- e1002743
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- OpenAlex
- W1998377233
- Cited by
- 516
- References
- 8
- FWCI
- 26.99
- Type
- journal article
- Language
- en
- Open access
- gold
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- Kembel, S. W., Wu, M., Eisen, J. A., & Green, J. L. (2012). Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance. PLoS Computational Biology. https://doi.org/10.1371/journal.pcbi.1002743
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