ArticleNature communications2022
Microbiome differential abundance methods produce different results across 38 datasets.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 615 papers, 2 of them syntheses that pooled it.
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Who cites it
615 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Gut, Oral, and Fungal Microbiota in Hypertension: A Multi-Compartment Systematic Review.International journal of molecular sciences · 2026Pooled it
- From culture-based to 16S sequencing approaches: oral microbiota changes in aging, frailty, and oral hypofunction - a systematic review.BMC oral health · 2026Pooled it
- Gut microbial diversity at baseline conditions the clinical, microbiome, and metabolic response to paraprobioticGut microbes · 2026Trial
- Resistant dextrin promotes beneficial fecal bacteria in high and low fiber diet populations: a randomized, double-blinded, controlled pilot study.Frontiers in nutrition · 2026Trial
- Systemic and gut microbiome changes with metformin and liraglutide in youth-onset type 2 diabetes: the MIGHTY study.Gut microbes · 2025Trial
- Early risk stratification of late-onset sepsis in very preterm infants by intestinal microbiota profiling: a multicenter case-control validation study.Gut microbes · 2026Article
- From gut to brain: data-driven evidence suggests causal contribution of gut-microbiota to major depressive disorder in humans.Gut microbes · 2026Article
- Article
- Characterization of oral microbiome in atopic dermatitis patients treated with dupilumab or upadacitinib.Clinical oral investigations · 2026Article
- Associations of Urinary Triclosan Concentrations Across Gestation and Childhood with the Adolescent Gut Microbiome.Environmental health perspectives · 2026Article
- Lung microbiome features associated with ICU delirium in mechanically ventilated patients: a secondary cohort analysis.Brain, behavior, & immunity - health · 2026Article
- Top-down/bottom-up consortia achieve robust γ-HCH degradation and reduced methanogenic contribution in wetlands.Applied and environmental microbiology · 2026Article
- Multi-kingdom gut microbiota analyses identify biomarkers of different types of pediatric short bowel syndrome.mSystems · 2026Article
- Soil microbial responses to long-term agricultural practices under no-till: insights from depth-resolved sampling of shallow soil layers.FEMS microbiology ecology · 2026Article
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.Genome biology · 2026Review
- Metabolite-Resolved Validation of Bacterial Organophosphate Transformation in Soil: A Critical Review of Selected Organophosphate Case Studies.Microorganisms · 2026Review
- The Biotechnological Applications of Marine Bacteria.Biology · 2026Review
- Tipping Points in Bacterial Richness Amplify Extracellular Enzyme Activities in Northern Peatlands.Global change biology · 2026Article
- IRS: iterative reference selection improves normalization of microbiome sequencing data.Briefings in bioinformatics · 2026Article
- Foundation Models for Microbiome Research: From Sequence Semantics to Community Dynamics and Multimodal World Models.Advanced genetics (Hoboken, N.J.) · 2026Review
555 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods are used interchangeably for this purpose in the literature. Yet, there are few large-scale studies systematically exploring the appropriateness of using these tools interchangeably, and the scale and significance of the differences between them. Here, we compare the performance of 14 differential abundance testing methods on 38 16S rRNA gene datasets with two sample groups. We test for differences in amplicon sequence variants and operational taxonomic units (ASVs) between these groups. Our findings confirm that these tools identified drastically different numbers and sets of significant ASVs, and that results depend on data pre-processing. For many tools the number of features identified correlate with aspects of the data, such as sample size, sequencing depth, and effect size of community differences. ALDEx2 and ANCOM-II produce the most consistent results across studies and agree best with the intersect of results from different approaches. Nevertheless, we recommend that researchers should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations.
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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.