RT Journal Article
SR Electronic
T1 Repeatability and reproducibility assessment in a large-scale population-based microbiota study: case study on human milk microbiota
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.04.20.052035
DO 10.1101/2020.04.20.052035
A1 Shirin Moossavi
A1 Kelsey Fehr
A1 Theo J. Moraes
A1 Ehsan Khafipour
A1 Meghan B. Azad
YR 2020
UL http://biorxiv.org/content/early/2020/04/23/2020.04.20.052035.abstract
AB Background Quality 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.Results In 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 reslults in each batch before merging them for downstream analysis.Conclusion This 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.Competing Interest StatementThe authors have declared no competing interest.MQCPMicrobiome Quality Control ProjectASVAmplicon sequencing variantRDARedundancy analysisPCoAPrincipal coordinate analysisCHILDCanadian Healthy Infant Longitudinal Development