ArticleMicrobiome2021
Repeatability and reproducibility assessment in a large-scale population-based microbiota study: case study on human milk microbiota.
Article in Microbiome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Basic Microbiome Analysis: Analytical Steps from Sampling to Sequencing.Microorganisms · 2026Review
- The application of AI-driven and engineered intratumoral microbes in cancer therapy.Journal of translational medicine · 2025Review
- Role of the upper airway microbiota in respiratory virus and bacterial pathobiont dynamics in the first year of life.Nature communications · 2025Article
- Microbiota of Healthy Dental Pulp Under the Omics Loupe.International journal of molecular sciences · 2025Article
- Differential microbial community assembly following co-housing versus microbiota transplant.The ISME journal · 2025Article
- Methodological approaches in 16S sequencing of female reproductive tract in fertility patients: a review.Journal of assisted reproduction and genetics · 2025Review
- Protocol: the International Milk Composition (IMiC) Consortium - a harmonized secondary analysis of human milk from four studies.Frontiers in nutrition · 2025Article
- Comparative study of milk microbiota and metabolome in long-lived dairy cows with different persistent production capacities.Frontiers in microbiology · 2025Article
- The impact of kit, environment, and sampling contamination on the observed microbiome of bovine milk.mSystems · 2024Article
- Prenatal vitamin C and fish oil supplement use are associated with human milk microbiota composition in the Canadian CHILD Cohort Study.Journal of nutritional science · 2024Article
- Similarity in milk microbiota in replicates.MicrobiologyOpen · 2023Article
- Applications of multi-omics analysis in human diseases.MedComm · 2023Review
- Article
- Interrogating the role of the milk microbiome in mastitis in the multi-omics era.Frontiers in microbiology · 2023Review
- Microbial Dark Matter: from Discovery to Applications.Genomics, proteomics & bioinformatics · 2022Review
- Composition and Functional Potential of the Human Mammary Microbiota Prior to and Following Breast Tumor Diagnosis.mSystems · 2022Article
- Systematically assessing microbiome-disease associations identifies drivers of inconsistency in metagenomic research.PLoS biology · 2022Article
- Maternal and early life exposures and their potential to influence development of the microbiome.Genome medicine · 2022Review
- Biological observations in microbiota analysis are robust to the choice of 16S rRNA gene sequencing processing algorithm: case study on human milk microbiota.BMC microbiology · 2020Article
- How longitudinal data can contribute to our understanding of host genetic effects on the gut microbiome.Gut microbesArticle
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Authors and funding
4 authors.
Funding
Abstract
backgroundQuality control including assessment of batch variabilities and confirmation of repeatability and reproducibility are integral component of high throughput omics studies including microbiome research. Batch effects can mask true biological results and/or result in irreproducible conclusions and interpretations. Low biomass samples in microbiome research are prone to reagent contamination; yet, quality control procedures for low biomass samples in large-scale microbiome studies are not well established.
resultsIn this study, we have proposed a framework for an in-depth step-by-step approach to address this gap. The framework consists of three independent stages: (1) verification of sequencing accuracy by assessing technical repeatability and reproducibility of the results using mock communities and biological controls; (2) contaminant removal and batch variability correction by applying a two-tier strategy using statistical algorithms (e.g. decontam) followed by comparison of the data structure between batches; and (3) corroborating the repeatability and reproducibility of microbiome composition and downstream statistical analysis. Using this approach on the milk microbiota data from the CHILD Cohort generated in two batches (extracted and sequenced in 2016 and 2019), we were able to identify potential reagent contaminants that were missed with standard algorithms and substantially reduce contaminant-induced batch variability. Additionally, we confirmed the repeatability and reproducibility of our results in each batch before merging them for downstream analysis.
conclusionThis study provides important insight to advance quality control efforts in low biomass microbiome research. Within-study quality control that takes advantage of the data structure (i.e. differential prevalence of contaminants between batches) would enhance the overall reliability and reproducibility of research in this field. Video abstract.
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