To rarefy or not to rarefy: Enhancing diversity analysis of microbial communities through next-generation sequencing and rarefying repeatedly

Abstract

The application of amplicon sequencing in water research provides a rapid and sensitive technique for microbial community analysis in a variety of environments ranging from freshwater lakes to water and wastewater treatment plants. It has revolutionized our ability to study DNA collected from environmental samples by eliminating the challenges associated with lab cultivation and taxonomic identification. DNA sequencing data consist of discrete counts of sequence reads, the total number of which is the library size. Samples may have different library sizes and thus, a normalization technique is required to meaningfully compare them. The process of randomly subsampling sequences to a selected normalized library size from the sample library—rarefying—is one such normalization technique. However, rarefying has been criticized as a normalization technique because data can be omitted through the exclusion of either excess sequences or entire samples, depending on the rarefied library size selected. Although it has been suggested that rarefying should be avoided altogether, we propose that repeatedly rarefying enables (i) characterization of the variation introduced to diversity analyses by this random subsampling and (ii) selection of smaller library sizes where necessary to incorporate all samples in the analysis. Rarefying may be a statistically valid normalization technique, but researchers should evaluate their data to make appropriate decisions regarding library size selection and subsampling type. The impact of normalized library size selection and rarefying with or without replacement in diversity analyses were evaluated herein.Highlights▪ Amplicon sequencing technology for environmental water samples is reviewed▪ Sequencing data must be normalized to allow comparison in diversity analyses▪ Rarefying normalizes library sizes by subsampling from observed sequences▪ Criticisms of data loss through rarefying can be resolved by rarefying repeatedly▪ Rarefying repeatedly characterizes errors introduced by subsampling sequences

Global Diversity and Biogeography of theZostera marinaMycobiome

“…To rarefy microbiome data is an ongoing scientific discussion (148)(149)(150)(151) and here we use both alternative normalization techniques and rarefaction. For the majority of the analyses, after investigating library sizes, rarefaction curves and to maintain sufficient replication across all sites (Figure S17), we utilized raw read counts, proportions, centered log-ratio, or Hellinger transformations on the data as appropriate when performing statistics and generating visualizations.…”

Bacterial Diversity in House Dust: Characterization of a Core Indoor Microbiome

“…Controls and low read count samples (only one sample, DSUK182) were removed, followed by rarefaction (without replacement) to 90% of the minimum sample read count (7,826 reads per sample) to normalise the library size across the samples. DNA sequencing data consist of discrete counts of sequence reads and the total number of which is the library size (Cameron et al, 2020). Library sizes can vary greatly between samples and thus the samples were normalised to remove bias and false inferences due to variations in library size.…”

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

“…Because the extent to which zeros compromised accurate estimation waned with increasing library size (Figure 1), a similar analysis was performed on amplicon sequencing data for six water samples from lakes. The samples (Cameron et al, 2020) featured library sizes between 10,000 and 30,000 and observation of 1,142 unique variants among the samples. All singleton counts had been zeroed and the completed ASV table had 3,342 rows (2,200 of which are all zeros associated with variants detected in other samples from the same study area).…”

“…Inference about source diversity is the ideal, but it is not possible with a multinomial relative abundance model unless the number of unique variants in the source is precisely known and there are many types of error in amplicon sequencing that are likely to invalidate this foundational model as discussed above. Rarefying repeatedly, a subsampling process to normalize library sizes among samples that is performed many times in order to characterize the variability introduced by rarefying (Cameron et al, 2020), satisfies these goals. When a sample is rarefied repeatedly down to a smaller library size (using sampling without replacement), it describes what data might have been obtained if only the smaller library size of sequence variants had been observed.…”