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

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 so...

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Autores principales: Kembel, Steven W., Wu, Martin, Eisen, Jonathan A., Green, Jessica L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3486904/
https://www.ncbi.nlm.nih.gov/pubmed/23133348
http://dx.doi.org/10.1371/journal.pcbi.1002743
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author Kembel, Steven W.
Wu, Martin
Eisen, Jonathan A.
Green, Jessica L.
author_facet Kembel, Steven W.
Wu, Martin
Eisen, Jonathan A.
Green, Jessica L.
author_sort Kembel, Steven W.
collection PubMed
description 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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spelling pubmed-34869042012-11-06 Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance Kembel, Steven W. Wu, Martin Eisen, Jonathan A. Green, Jessica L. PLoS Comput Biol Research Article 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. Public Library of Science 2012-10-25 /pmc/articles/PMC3486904/ /pubmed/23133348 http://dx.doi.org/10.1371/journal.pcbi.1002743 Text en © 2012 Kembel et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Kembel, Steven W.
Wu, Martin
Eisen, Jonathan A.
Green, Jessica L.
Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance
title Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance
title_full Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance
title_fullStr Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance
title_full_unstemmed Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance
title_short Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance
title_sort incorporating 16s gene copy number information improves estimates of microbial diversity and abundance
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3486904/
https://www.ncbi.nlm.nih.gov/pubmed/23133348
http://dx.doi.org/10.1371/journal.pcbi.1002743
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