Enhancing diversity analysis by repeatedly rarefying next generation sequencing data describing microbial communities

Abstract Amplicon sequencing has revolutionized our ability to study DNA collected from environmental samples by providing a rapid and sensitive technique for microbial community analysis that eliminates the challenges associated with lab cultivation and taxonomic identification through microscopy....

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Autores principales:	Ellen S. Cameron, Philip J. Schmidt, Benjamin J.-M. Tremblay, Monica B. Emelko, Kirsten M. Müller
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ac2f8ee28b1a482b837a75acc69d8a76
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spelling	oai:doaj.org-article:ac2f8ee28b1a482b837a75acc69d8a762021-11-21T12:17:19ZEnhancing diversity analysis by repeatedly rarefying next generation sequencing data describing microbial communities10.1038/s41598-021-01636-12045-2322https://doaj.org/article/ac2f8ee28b1a482b837a75acc69d8a762021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01636-1https://doaj.org/toc/2045-2322Abstract Amplicon sequencing has revolutionized our ability to study DNA collected from environmental samples by providing a rapid and sensitive technique for microbial community analysis that eliminates the challenges associated with lab cultivation and taxonomic identification through microscopy. In water resources management, it can be especially useful to evaluate ecosystem shifts in response to natural and anthropogenic landscape disturbances to signal potential water quality concerns, such as the detection of toxic cyanobacteria or pathogenic bacteria. Amplicon sequencing data consist of discrete counts of sequence reads, the sum of which is the library size. Groups of samples typically have different library sizes that are not representative of biological variation; library size normalization is required to meaningfully compare diversity between them. Rarefaction is a widely used normalization technique that involves the random subsampling of sequences from the initial sample library to a selected normalized library size. This process is often dismissed as statistically invalid because subsampling effectively discards a portion of the observed sequences, yet it remains prevalent in practice and the suitability of rarefying, relative to many other normalization approaches, for diversity analysis has been argued. Here, repeated rarefying is proposed as a tool to normalize library sizes for diversity analyses. This enables (i) proportionate representation of all observed sequences and (ii) characterization of the random variation introduced to diversity analyses by rarefying to a smaller library size shared by all samples. While many deterministic data transformations are not tailored to produce equal library sizes, repeatedly rarefying reflects the probabilistic process by which amplicon sequencing data are obtained as a representation of the amplified source microbial community. Specifically, it evaluates which data might have been obtained if a particular sample’s library size had been smaller and allows graphical representation of the effects of this library size normalization process upon diversity analysis results.Ellen S. CameronPhilip J. SchmidtBenjamin J.-M. TremblayMonica B. EmelkoKirsten M. MüllerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ellen S. Cameron Philip J. Schmidt Benjamin J.-M. Tremblay Monica B. Emelko Kirsten M. Müller Enhancing diversity analysis by repeatedly rarefying next generation sequencing data describing microbial communities
description	Abstract Amplicon sequencing has revolutionized our ability to study DNA collected from environmental samples by providing a rapid and sensitive technique for microbial community analysis that eliminates the challenges associated with lab cultivation and taxonomic identification through microscopy. In water resources management, it can be especially useful to evaluate ecosystem shifts in response to natural and anthropogenic landscape disturbances to signal potential water quality concerns, such as the detection of toxic cyanobacteria or pathogenic bacteria. Amplicon sequencing data consist of discrete counts of sequence reads, the sum of which is the library size. Groups of samples typically have different library sizes that are not representative of biological variation; library size normalization is required to meaningfully compare diversity between them. Rarefaction is a widely used normalization technique that involves the random subsampling of sequences from the initial sample library to a selected normalized library size. This process is often dismissed as statistically invalid because subsampling effectively discards a portion of the observed sequences, yet it remains prevalent in practice and the suitability of rarefying, relative to many other normalization approaches, for diversity analysis has been argued. Here, repeated rarefying is proposed as a tool to normalize library sizes for diversity analyses. This enables (i) proportionate representation of all observed sequences and (ii) characterization of the random variation introduced to diversity analyses by rarefying to a smaller library size shared by all samples. While many deterministic data transformations are not tailored to produce equal library sizes, repeatedly rarefying reflects the probabilistic process by which amplicon sequencing data are obtained as a representation of the amplified source microbial community. Specifically, it evaluates which data might have been obtained if a particular sample’s library size had been smaller and allows graphical representation of the effects of this library size normalization process upon diversity analysis results.
format	article
author	Ellen S. Cameron Philip J. Schmidt Benjamin J.-M. Tremblay Monica B. Emelko Kirsten M. Müller
author_facet	Ellen S. Cameron Philip J. Schmidt Benjamin J.-M. Tremblay Monica B. Emelko Kirsten M. Müller
author_sort	Ellen S. Cameron
title	Enhancing diversity analysis by repeatedly rarefying next generation sequencing data describing microbial communities
title_short	Enhancing diversity analysis by repeatedly rarefying next generation sequencing data describing microbial communities
title_full	Enhancing diversity analysis by repeatedly rarefying next generation sequencing data describing microbial communities
title_fullStr	Enhancing diversity analysis by repeatedly rarefying next generation sequencing data describing microbial communities
title_full_unstemmed	Enhancing diversity analysis by repeatedly rarefying next generation sequencing data describing microbial communities
title_sort	enhancing diversity analysis by repeatedly rarefying next generation sequencing data describing microbial communities
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ac2f8ee28b1a482b837a75acc69d8a76
work_keys_str_mv	AT ellenscameron enhancingdiversityanalysisbyrepeatedlyrarefyingnextgenerationsequencingdatadescribingmicrobialcommunities AT philipjschmidt enhancingdiversityanalysisbyrepeatedlyrarefyingnextgenerationsequencingdatadescribingmicrobialcommunities AT benjaminjmtremblay enhancingdiversityanalysisbyrepeatedlyrarefyingnextgenerationsequencingdatadescribingmicrobialcommunities AT monicabemelko enhancingdiversityanalysisbyrepeatedlyrarefyingnextgenerationsequencingdatadescribingmicrobialcommunities AT kirstenmmuller enhancingdiversityanalysisbyrepeatedlyrarefyingnextgenerationsequencingdatadescribingmicrobialcommunities
_version_	1718419086853013504

Enhancing diversity analysis by repeatedly rarefying next generation sequencing data describing microbial communities

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