The community ecology perspective of omics data.
Community ecology
Molecular ecology
Multivariate statistics
Journal
Microbiome
ISSN: 2049-2618
Titre abrégé: Microbiome
Pays: England
ID NLM: 101615147
Informations de publication
Date de publication:
13 12 2022
13 12 2022
Historique:
received:
03
04
2022
accepted:
10
11
2022
entrez:
12
12
2022
pubmed:
13
12
2022
medline:
15
12
2022
Statut:
epublish
Résumé
The measurement of uncharacterized pools of biological molecules through techniques such as metabarcoding, metagenomics, metatranscriptomics, metabolomics, and metaproteomics produces large, multivariate datasets. Analyses of these datasets have successfully been borrowed from community ecology to characterize the molecular diversity of samples (ɑ-diversity) and to assess how these profiles change in response to experimental treatments or across gradients (β-diversity). However, sample preparation and data collection methods generate biases and noise which confound molecular diversity estimates and require special attention. Here, we examine how technical biases and noise that are introduced into multivariate molecular data affect the estimation of the components of diversity (i.e., total number of different molecular species, or entities; total number of molecules; and the abundance distribution of molecular entities). We then explore under which conditions these biases affect the measurement of ɑ- and β-diversity and highlight how novel methods commonly used in community ecology can be adopted to improve the interpretation and integration of multivariate molecular data. Video Abstract.
Identifiants
pubmed: 36510248
doi: 10.1186/s40168-022-01423-8
pii: 10.1186/s40168-022-01423-8
pmc: PMC9746134
doi:
Types de publication
Video-Audio Media
Letter
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
225Informations de copyright
© 2022. The Author(s).
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