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

Abstract

38 Background: Quality control including assessment of batch variabilities and confirmation of 39 repeatability and reproducibility are integral component of high throughput omics studies 40 including microbiome research. Batch effects can mask true biological results and/or result in 41 irreproducible conclusions and interpretations. Low biomass samples in microbiome research are 42 prone to reagent contamination; yet, quality control procedures for low biomass samples in large-43 scale microbiome studies are not well established. 44Results: In this study we have proposed a framework for an in-depth step-by-step approach to 45 address this gap. The framework consists of three independent stages: 1) verification of 46 sequencing accuracy by assessing technical repeatability and reproducibility of the results using 47

Associations and recovery dynamics of the nasopharyngeal microbiota during influenza-like illness in the aging population

“…Iteratively going through every step, i.e., decontamination using decontam package, checking for abundances in negative controls, and identifying prevalent and dominant contaminants that were abundant in both controls and samples using co-occurrence analysis, aided in removal of a large number of potentially contaminating taxa. While the decontam package identified several contaminants, requirement of additional steps after decontamination have been recently reported, and in our study, we also noticed the importance of detailed investigation of contaminants [27,49]. Furthermore, taxonomic investigation as well as looking for isolation sources for doubtful taxa in well curated public resources like the Bacterial Diversity Metadatabase (BacDive) proved as a useful tool for rational choice on filtering [31].…”

Breastmilk Feeding Practices Are Associated with the Co-Occurrence of Bacteria in Mothers’ Milk and the Infant Gut: the CHILD Cohort Study

“…Data analysis was conducted in R (R Core Team, 2018) and preprocessing of the ASV table was conducted using the Phyloseq package (McMurdie and Holmes, 2013). Potential reagent contaminants of milk microbiota were identified and removed as previously described (Moossavi et al, 2020). ASVs belonging to the genus Halomonas were highly present in negative controls for the gut microbiota [median read-count (IQR), 214.0 (122.0-271.0)] and thus removed.…”

Microbiome features differentiating unsupervised-stratification based clusters of patients with abnormal glycometabolism

“…The raw sequence data were demultiplexed, denoised, and filtered for chimeric reads with the DADA2 plugin (48) to obtain the frequency table and representative sequence file of amplicon sequence variants (ASVs). After we removed the ASVs considered to be contaminants (49), the decontamination table composed of 353 sample and 5,448 ASVs was downsized to 10,000,000 to standardize sequence depth. We used the representative sequence file for taxonomic annotation using the SILVA database (version 138).…”