ReviewNature protocols2026
A workflow for statistical analysis and visualization of microbiome omics data using the R microeco package.
Review in Nature protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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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.
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Who cites it
21 citing papers in PubMed.
- Top-down/bottom-up consortia achieve robust γ-HCH degradation and reduced methanogenic contribution in wetlands.Applied and environmental microbiology · 2026Article
- Microbial Diversity and Hydrocarbon-Oxidizing Bacteria in Coastal Waters and Sands Contaminated by the Fuel Oil Spill in the Black Sea.Microorganisms · 2026Article
- Species-Specific Pathogens Drive Distinct Structural and Inter-Kingdom Dynamics in the Mulberry Leaf Microbiome.Microorganisms · 2026Article
- Life-Cycle Diphenyl Phosphate (DPhP) Exposure Reveals Sex-Differential Functional Alterations in the Zebrafish Gut Microbiota.Biology · 2026Article
- Stage-Dependent Dynamics and Assembly Processes ofMicroorganisms · 2026Article
- Free and Protected Protease in the Diet of Lactating Jersey Cows: Effects on Performance, Milk Quality, Metabolism, Nutrient Digestibility, Microbiota, and Ruminal Environment.Animals : an open access journal from MDPI · 2026Article
- The Cervical Microbiome in Hispanic Populations in Texas and Puerto Rico with and without Cervical Dysplasia.Research square · 2026Article
- Article
- Common genetic variants in vitamin D metabolism-related genes affect gut microbiota diversity in postmenopausal women.Genes & nutrition · 2026Article
- Sex differences in hedonic feeding and characterizing the effects of antibiotic-induced microbiome disruption.Research square · 2026Article
- Iron-Containing Flocs Derived from Environmental Emergency Response Influenced Nitrogen Cycling Driven by Microorganisms in River Sediments.Microorganisms · 2026Article
- Temporal and spatial shifts in the foliar fungal endophyte community of Norway spruce (Picea abies) over 150 years in Finland.Environmental microbiome · 2026Article
- Fusobacterium nucleatum manipulates host autophagy to promote its intracellular survival and treatment resistance in nasopharyngeal carcinoma.Molecular cancer · 2026Article
- Daily fiber supplementation after bariatric surgery demonstrates tolerability and influence on the fecal gut microbiome.Frontiers in nutrition · 2026Article
- Global meta-analysis of drought associated rice rhizosphere microbiome data and soil application of selected bacteria reveals potential ofFrontiers in plant science · 2026Article
- Bioinformatic tools for microbiome analysis: from raw sequences to biological insights.Frontiers in microbiology · 2026Review
- The gut microbiome in early life predicts malaria susceptibility.Frontiers in cellular and infection microbiology · 2026Article
- Topological characteristics and longitudinal dynamics of co-abundance networks involving beneficial commensal bacteria in the pig gut microbiome and its association with average daily gain.Frontiers in microbiology · 2026Article
- Gut microbiome dynamics and cardiometabolic disease progression: evidence from a secondary data analysis of pooled clinical datasets.Frontiers in microbiology · 2026Article
- Habitat Diversity Sustains Ecosystem Functioning in Plateau Arid-Region Wetlands.Ecology and evolution · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The increasing complexity of experimental designs and the volume of data in the microbiome field, along with the diversification of omics data types, pose substantial challenges to statistical analysis and visualization. Here we present a step-by-step protocol based on the R microeco package ( https://github.com/ChiLiubio/microeco ) that details the statistical analysis and visualization of microbiome data. The omics data types shown consist of amplicon sequencing data, metagenomic sequencing data and nontargeted metabolomics data. The analysis of amplicon sequencing data specifically involves data preprocessing and normalization, core taxa, alpha diversity, beta diversity, differential abundance testing and machine learning. We consider various data analysis scenarios in each section to exhibit the comprehensiveness of the protocol. We emphasize that different normalized data produced by various methods are selected for subsequent analysis of each part based on the best analytical practices. Additionally, in the differential abundance test analysis, we adopt parametric community simulation to enable the performance evaluation of various testing approaches. For the analysis of metagenomic data, the focus is on how bioinformatic analysis data are read and preprocessed, which refers to the major usage differences from amplicon sequencing data. For metabolomics data, we mainly demonstrate the differential test, machine learning and association analysis with microbial abundances. To address some complex analyses, this protocol extensively combines different types of methods to build an analysis pipeline. This protocol is more comprehensive and scalable compared with alternative methods. The provided R codes can run in about 6 h on a laptop computer.
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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.