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Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance

Overview of attention for article published in PLoS Computational Biology, October 2012
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Title
Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance
Published in
PLoS Computational Biology, October 2012
DOI 10.1371/journal.pcbi.1002743
Pubmed ID
Authors

Steven W. Kembel, Martin Wu, Jonathan A. Eisen, Jessica L. Green

Abstract

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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Geographical breakdown

Country Count As %
United States 36 4%
Canada 7 <1%
Germany 6 <1%
Belgium 5 <1%
United Kingdom 5 <1%
France 4 <1%
Spain 3 <1%
Denmark 3 <1%
Mexico 3 <1%
Other 18 2%
Unknown 883 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 272 28%
Researcher 206 21%
Student > Master 141 14%
Student > Bachelor 66 7%
Student > Doctoral Student 55 6%
Other 117 12%
Unknown 116 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 480 49%
Biochemistry, Genetics and Molecular Biology 108 11%
Environmental Science 88 9%
Immunology and Microbiology 36 4%
Engineering 24 2%
Other 81 8%
Unknown 156 16%