Establishing microbial composition measurement standards with reference frames

Differential abundance analysis is controversial throughout microbiome research. Gold standard approaches require laborious measurements of total microbial load, or absolute number of microorganisms, to accurately determine taxonomic shifts. Therefore, most studies rely on relative abundance data. H...

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Veröffentlicht in:Nature communications Jg. 10; H. 1; S. 2719 - 11
Hauptverfasser: Morton, James T., Marotz, Clarisse, Washburne, Alex, Silverman, Justin, Zaramela, Livia S., Edlund, Anna, Zengler, Karsten, Knight, Rob
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 20.06.2019
Nature Publishing Group
Nature Portfolio
Schlagworte:
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631/326/2565/2134
631/326/41/2142
639/705/1041
Abundance
Alliances
Atopic dermatitis
Bacteria - genetics
Bacteria - isolation & purification
Bacterial Load - standards
Computer Simulation - standards
Data Analysis
Datasets as Topic
Dermatitis
Dermatitis, Atopic - microbiology
Eczema
Feasibility Studies
Healthy Volunteers
High-Throughput Nucleotide Sequencing - statistics & numerical data
Humanities and Social Sciences
Humans
Metagenome - genetics
Microbiomes
Microbiota - genetics
Microorganisms
Models, Biological
multidisciplinary
Reference Standards
Relative abundance
RNA, Ribosomal, 16S - isolation & purification
Saliva - microbiology
Science
Science (multidisciplinary)
Sequences
Soil Microbiology
ISSN:2041-1723, 2041-1723
Online-Zugang:Volltext
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Zusammenfassung:Differential abundance analysis is controversial throughout microbiome research. Gold standard approaches require laborious measurements of total microbial load, or absolute number of microorganisms, to accurately determine taxonomic shifts. Therefore, most studies rely on relative abundance data. Here, we demonstrate common pitfalls in comparing relative abundance across samples and identify two solutions that reveal microbial changes without the need to estimate total microbial load. We define the notion of “reference frames”, which provide deep intuition about the compositional nature of microbiome data. In an oral time series experiment, reference frames alleviate false positives and produce consistent results on both raw and cell-count normalized data. Furthermore, reference frames identify consistent, differentially abundant microbes previously undetected in two independent published datasets from subjects with atopic dermatitis. These methods allow reassessment of published relative abundance data to reveal reproducible microbial changes from standard sequencing output without the need for new assays. Most microbiome studies make conclusions based on changes in relative abundance of taxa, inferred from sequencing data. Here, the authors highlight common pitfalls in comparing relative abundance across samples, and identify solutions that reveal microbial changes without the need to estimate total microbial load.
Bibliographie:ObjectType-Article-1
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-019-10656-5