ArticleGigaScience2018
Benchmarking taxonomic assignments based on 16S rRNA gene profiling of the microbiota from commonly sampled environments.
Article in GigaScience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 2 of them syntheses that pooled it.
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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.
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.
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Who cites it
63 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The association between early-life gut microbiota and childhood respiratory diseases: a systematic review.The Lancet. Microbe · 2022Pooled it
- MICHELINdb: a web-based tool for mining of helminth-microbiota interaction datasets, and a meta-analysis of current research.Microbiome · 2020Pooled it
- Can the leafhopper microbiome unlock new strategies for its control?Journal of economic entomology · 2026Review
- Evaluating 16S metagenomics data analysis pipelines using an Ion Torrent sequencer for human gut microbiome research.BMC microbiology · 2026Article
- Gut Microbiome Dysbiosis in Metabolic Syndrome: Current Evidence and Emerging Perspectives.Nutrients · 2026Review
- Towards standardization in pig microbiome research based on a comprehensive twenty-year review.Animal microbiome · 2026Review
- Pasture diversity and regenerative management shape ruminant gut microbiome dynamics within a real multi-year farming system.Frontiers in microbiology · 2026Article
- Bioinformatic tools for microbiome analysis: from raw sequences to biological insights.Frontiers in microbiology · 2026Review
- Distinct gut and oral microbiome patterns associated with dyslexia in a family-based cohort: A preliminary exploratory study.PloS one · 2026Article
- Co-Fermentation withFoods (Basel, Switzerland) · 2025Article
- Robust 16S rRNA classification based on a compressed LCA index.Genome research · 2025Article
- Targeted probe capture metagenomics-enabled surveillance of multidrug-resistant organisms and antimicrobial resistance genes in post-handwashing areas of public washrooms.Environmental microbiome · 2025Article
- Comparison of QIIME1 and QIIME2 for Analyzing Fungal Samples from Various Built Environments.Microorganisms · 2025Article
- Toward standardized methods in canine vaginal microbiome research: evaluation of storage, host DNA depletion, and database selection.Microbiology spectrum · 2025Article
- Consistent microbial insights across sequencing methods in soil studies: the role of reference taxonomies.mSystems · 2025Article
- Do we need a standardized 16S rRNA gene amplicon sequencing analysis protocol for poultry microbiota research?Poultry science · 2025Review
- Comparison of different microbiome analysis pipelines to validate their reproducibility of gastric mucosal microbiome composition.mSystems · 2025Article
- 16S rRNA metabarcoding applied to the microbiome of insect products (novel food): a comparative analysis of three reference databases.Italian journal of food safety · 2025Article
- Christensenella minuta protects and restores intestinal barrier in a colitis mouse model by regulating inflammation.NPJ biofilms and microbiomes · 2024Article
- Current Trends and Challenges of Microbiome Research in Prostate Cancer.Current oncology reports · 2024Review
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Taxonomic profiling of ribosomal RNA (rRNA) sequences has been the accepted norm for inferring the composition of complex microbial ecosystems. Quantitative Insights Into Microbial Ecology (QIIME) and mothur have been the most widely used taxonomic analysis tools for this purpose, with MAPseq and QIIME 2 being two recently released alternatives. However, no independent and direct comparison between these four main tools has been performed. Here, we compared the default classifiers of MAPseq, mothur, QIIME, and QIIME 2 using synthetic simulated datasets comprised of some of the most abundant genera found in the human gut, ocean, and soil environments. We evaluate their accuracy when paired with both different reference databases and variable sub-regions of the 16S rRNA gene. Findings: We show that QIIME 2 provided the best recall and F-scores at genus and family levels, together with the lowest distance estimates between the observed and simulated samples. However, MAPseq showed the highest precision, with miscall rates consistently <2%. Notably, QIIME 2 was the most computationally expensive tool, with CPU time and memory usage almost 2 and 30 times higher than MAPseq, respectively. Using the SILVA database generally yielded a higher recall than using Greengenes, while assignment results of different 16S rRNA variable sub-regions varied up to 40% between samples analysed with the same pipeline. Conclusions: Our results support the use of either QIIME 2 or MAPseq for optimal 16S rRNA gene profiling, and we suggest that the choice between the two should be based on the level of recall, precision, and/or computational performance required.
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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.