Repeatability and reproducibility assessment in a large-scale population-based microbiota study: case study on human milk microbiota

Microbiome

Kelsey Fehr¹  Meghan B. Azad²  Ehsan Khafipour³  Shirin Moossavi⁴ 

[1] Children’s Hospital Research Institute of Manitoba, Winnipeg, MB, Canada;Department of Pediatrics and Child Health, University of Manitoba, Winnipeg, MB, Canada;Children’s Hospital Research Institute of Manitoba, Winnipeg, MB, Canada;Developmental Origins of Chronic Diseases in Children Network (DEVOTION), Winnipeg, MB, Canada;Department of Pediatrics and Child Health, University of Manitoba, Winnipeg, MB, Canada;Department of Animal Science, University of Manitoba, Winnipeg, MB, Canada;Microbiome Research and Technical Support, Cargill Animal Nutrition, Diamond V brand, Cedar Rapids, USA;Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, MB, Canada;Children’s Hospital Research Institute of Manitoba, Winnipeg, MB, Canada;Developmental Origins of Chronic Diseases in Children Network (DEVOTION), Winnipeg, MB, Canada;Digestive Oncology Research Center, Digestive Disease Research Institute, Tehran University of Medical Sciences, Tehran, Iran;Department of Physiology and Pharmacology & Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB, Canada;

关键词: Decontam;    Reagent contaminant;    Batch variation;    Repeatability;    Reproducibility;    Microbiome;    Milk microbiota;    Human milk;    CHILD cohort;   

DOI  :  10.1186/s40168-020-00998-4

来源: Springer

【 摘 要 】

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.Dksa_EC4VEZpmNY5pbL1BCVideo abstract

【 授权许可】

CC BY