ArticleMicrobiome2022
The community ecology perspective of omics data.
Article in Microbiome, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Metagenomic Insights Into Red Sea Biodiversity Across the Web of Life.Environmental microbiology · 2026Article
- The gut microbiome and ageing trajectories: mechanisms and clinical implications.Nature reviews. Endocrinology · 2026Review
- Fungal ecology in the age of 'omics.The New phytologist · 2026Review
- Navigating the data processing for cytometry-based single-cell proteomics.Nature protocols · 2026Review
- A practical guide to characterising ecological coexistence.Biological reviews of the Cambridge Philosophical Society · 2026Review
- Single cell viral tagging ofGut microbes · 2025Article
- Integrated 16S rDNA and Metabolomics Analysis Unveils Dietary Fiber-Induced Changes in Small Intestinal Microbiota and Metabolites of Pigs.Veterinary sciences · 2025Article
- Short Read Lengths Recover Ecological Patterns in 16S rRNA Gene Amplicon Data.Molecular ecology resources · 2025Article
- Article
- Review
- Article
- Rapid taxonomic categorization of short, abundant virus sequences for ecological analyses.Ecology and evolution · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.