Ensuring That Fundamentals of Quantitative Microbiology Are Reflected in Microbial Diversity Analyses Based on Next-Generation Sequencing

Abstract

Diversity analysis of amplicon sequencing data has mainly been limited to plug-in estimates calculated using normalized data to obtain a single value of an alpha diversity metric or a single point on a beta diversity ordination plot for each sample. As recognized for count data generated using classical microbiological methods, amplicon sequence read counts obtained from a sample are random data linked to source properties (e.g., proportional composition) by a probabilistic process. Thus, diversity analysis has focused on diversity exhibited in (normalized) samples rather than probabilistic inference about source diversity. This study applies fundamentals of statistical analysis for quantitative microbiology (e.g., microscopy, plating, and most probable number methods) to sample collection and processing procedures of amplicon sequencing methods to facilitate inference reflecting the probabilistic nature of such data and evaluation of uncertainty in diversity metrics. Following description of types of random error, mechanisms such as clustering of microorganisms in the source, differential analytical recovery during sample processing, and amplification are found to invalidate a multinomial relative abundance model. The zeros often abounding in amplicon sequencing data and their implications are addressed, and Bayesian analysis is applied to estimate the source Shannon index given unnormalized data (both simulated and experimental). Inference about source diversity is found to require knowledge of the exact number of unique variants in the source, which is practically unknowable due to library size limitations and the inability to differentiate zeros corresponding to variants that are actually absent in the source from zeros corresponding to variants that were merely not detected. Given these problems with estimation of diversity in the source even when the basic multinomial model is valid, diversity analysis at the level of samples with normalized library sizes is discussed.

Early Seasonal Increases and Persistence in Relative Abundance of Potentially Toxic Cyanobacteria: Concerning Impacts of Extended Ice-Free Periods in Northern Temperate Lakes

“…For cyanobacterial community analysis, ASVs classified as Cyanobacteria at the phylum level were filtered to create libraries consisting of only cyanobacteria classified sequences. Cyanobacterial libraries were repeatedly rarefied to a normalized size of 824 reads using mirlyn (Cameron & Tremblay, 2020) to address challenges of amplicon sequencing partially representing source diversity (Schmidt et al, 2022).…”

“…Sampling details are provided in Table S2. As described in Cameron et al (2022), DNA extraction was performed using the DNeasy PowerSoil Kit (QIAGEN Inc., Venlo, Netherlands), quantified using a NanoDrop spectrophotometer (Table S2; absolute values were only accurate at DNA concentrations of more than 10ng/µL, and submitted to a commercial laboratory (Metagenom Bio Inc.,Waterloo, ON) for amplicon sequencing of the V4 region of the 16S rRNA gene (515FB:[GTGYCAGCMGCCGCGGTAA]; 806RB -[GGACTACNVGGGTWTCTAAT]; Walters et al, 2015) sequencing using the Illumina MiSeq platform (Illumina Inc., San Diego, United States).…”

“…Water samples were collected and filtered following the approach described in Cameron et al (2022). They were collected at Secchi depth (during ice-free periods) and 0.25 m below ice (during periods of ice cover), at the deepest locations in Big Turkey, Little Turkey, and Wishart Lakes between July 2018 and January 2020.…”

Capturing marine microbiomes and environmental DNA: A field sampling guide

“…Furthermore, the optimal balance of tradeoffs may depend on the subsequent molecular processing method. For example, the DNA required to capture microbiome diversity ( Schmidt et al, 2022 ) from marker gene amplicon (metabarcoding) or shotgun sequencing likely differs from that needed to successfully conduct a qPCR assay for a specific target (e.g., Andruszkiewicz et al, 2020 ; Roux et al, 2020 ; Rourke et al, 2022 ).…”

Realizing the value in “non-standard” parts of the qPCR standard curve by integrating fundamentals of quantitative microbiology

“…For example, it should be possible to describe how precise an estimated concentration or standard curve model parameter is with interval estimates. This can be achieved through probabilistic modeling and, given the microbial context of qPCR, should align with established approaches to interpreting other types of quantitative microbiology data and the mechanisms behind observed variability (e.g., Student, 1907;McCrady, 1915;Fisher et al, 1922;Eisenhart and Wilson, 1943;Nahrstedt and Gimbel, 1996;Schmidt et al, 2022). Linear (or log-linear) regression, coupled with a parametric assumption about the distribution of residuals, is a type of probabilistic model; however, log-linear regression has chiefly been used as a means to an end in qPCR to fit a deterministic calibration curve rather than as a probabilistic model (with one exception being Tellinghuisen and Spiess, 2014).…”