Emelko, M. B. | bioRxiv Science

bioRxiv2021

Ensuring that fundamentals of quantitative microbiology are reflected in microbial diversity analyses based on next-generation sequencing

Diversity analysis of amplicon sequencing data is mainly 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, read counts obtained from a sample are random data linked to source properties by a probabilistic process. Thus, diversity analysis has focused on diversity of (normalized) samples rather than probabilistic inference about source diversity. This study applies fundamentals of statistical analysis for quantitative microbiology (e.g., microscopy, plating, 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. Types of random error are described and 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 real). 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, sample-level diversity analysis approaches are discussed. HighlightsO_LIRandom error in amplicon sequencing method should be considered in diversity analysis C_LIO_LIClustering, amplification, and differential recovery distort sample diversity C_LIO_LIThe multinomial model for compositional count data is compromised by amplification C_LIO_LIThere are three types of zeros in amplicon sequencing data, including missing zeros C_LIO_LISource alpha diversity estimates are biased by unknown number of unique variants C_LI

bioinformatics↗

bioRxiv2020

To rarefy or not to rarefy: Enhancing microbial community analysis through next-generation sequencing

Amplicon sequencing has revolutionized our ability to study DNA collected from environmental samples by providing a rapid and sensitive technique for microbial community analysis that eliminates the challenges associated with lab cultivation and taxonomic identification through microscopy. In water resources management, it can be especially useful to evaluate ecosystem shifts in response to natural and anthropogenic landscape disturbances to signal potential water quality concerns, such as the detection of toxic cyanobacteria or pathogenic bacteria. Amplicon sequencing data consist of discrete counts of sequence reads, the sum of which is the library size. Groups of samples typically have different library sizes that are not representative of biological variation; library size normalization is required to meaningfully compare diversity between them. Rarefaction is a widely used normalization technique that involves the random subsampling of sequences from the initial sample library to a selected normalized library size. Rarefying is often dismissed as statistically invalid because subsampling effectively discards a portion of the observed sequences. Nonetheless, it remains prevalent in practice. Notably, the superiority of rarefying relative to many other normalization approaches has been argued in diversity analysis. Here, repeated rarefying is proposed as a tool for diversity analyses to normalize library sizes. This enables (i) proportionate representation of all observed sequences and (ii) characterization of the random variation introduced to diversity analyses by rarefying to a smaller library size shared by all samples. While many deterministic data transformations are not tailored to produce equal library sizes, repeatedly rarefying reflects the probabilistic process by which amplicon sequencing data are obtained as a representation of the source microbial community. Specifically, it evaluates which data might have been obtained if a particular samples library size had been smaller and allows graphical representation of the effects of this library size normalization process upon diversity analysis results.

bioinformatics↗