Quantifying spatiotemporal variability and noise in absolute microbiota abundances using replicate sampling.


Journal

Nature methods
ISSN: 1548-7105
Titre abrégé: Nat Methods
Pays: United States
ID NLM: 101215604

Informations de publication

Date de publication:
08 2019
Historique:
received: 16 08 2018
accepted: 24 05 2019
pubmed: 17 7 2019
medline: 9 11 2019
entrez: 17 7 2019
Statut: ppublish

Résumé

Metagenomic sequencing has enabled detailed investigation of diverse microbial communities, but understanding their spatiotemporal variability remains an important challenge. Here, we present decomposition of variance using replicate sampling (DIVERS), a method based on replicate sampling and spike-in sequencing. The method quantifies the contributions of temporal dynamics, spatial sampling variability, and technical noise to the variances and covariances of absolute bacterial abundances. We applied DIVERS to investigate a high-resolution time series of the human gut microbiome and a spatial survey of a soil bacterial community in Manhattan's Central Park. Our analysis showed that in the gut, technical noise dominated the abundance variability for nearly half of the detected taxa. DIVERS also revealed substantial spatial heterogeneity of gut microbiota, and high temporal covariances of taxa within the Bacteroidetes phylum. In the soil community, spatial variability primarily contributed to abundance fluctuations at short time scales (weeks), while temporal variability dominated at longer time scales (several months).

Identifiants

pubmed: 31308552
doi: 10.1038/s41592-019-0467-y
pii: 10.1038/s41592-019-0467-y
pmc: PMC7219825
mid: NIHMS1570723
doi:

Substances chimiques

RNA, Ribosomal, 16S 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

731-736

Subventions

Organisme : NIAID NIH HHS
ID : R01 AI132403
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK118044
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM079759
Pays : United States
Organisme : NIGMS NIH HHS
ID : T32 GM007367
Pays : United States

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Auteurs

Brian W Ji (BW)

Department of Systems Biology, Columbia University, New York, NY, USA.
Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, NY, USA.

Ravi U Sheth (RU)

Department of Systems Biology, Columbia University, New York, NY, USA.
Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, NY, USA.

Purushottam D Dixit (PD)

Department of Systems Biology, Columbia University, New York, NY, USA.

Yiming Huang (Y)

Department of Systems Biology, Columbia University, New York, NY, USA.
Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, NY, USA.

Andrew Kaufman (A)

Department of Systems Biology, Columbia University, New York, NY, USA.

Harris H Wang (HH)

Department of Systems Biology, Columbia University, New York, NY, USA. hw2429@cumc.columbia.edu.
Department of Pathology and Cell Biology, Columbia University, New York, NY, USA. hw2429@cumc.columbia.edu.

Dennis Vitkup (D)

Department of Systems Biology, Columbia University, New York, NY, USA. dv2121@cumc.columbia.edu.
Department of Biomedical Informatics, Columbia University, New York, NY, USA. dv2121@cumc.columbia.edu.

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Classifications MeSH