Daring to be differential: metabarcoding analysis of soil and plant-related microbial communities using amplicon sequence variants and operational taxonomical units

Abstract

Background Microorganisms are not only indispensable to ecosystem functioning, they are also keystones for emerging technologies. In the last 15 years, the number of studies on environmental microbial communities has increased exponentially due to advances in sequencing technologies, but the large amount of data generated remains difficult to analyze and interpret. Recently, metabarcoding analysis has shifted from clustering reads using Operational Taxonomical Units (OTUs) to Amplicon Sequence Variants (ASVs). Differences between these methods can seriously affect the biological interpretation of metabarcoding data, especially in ecosystems with high microbial diversity, as the methods are benchmarked based on low diversity datasets. Results In this work we have thoroughly examined the differences in community diversity, structure, and complexity between the OTU and ASV methods. We have examined culture-based mock and simulated datasets as well as soil- and plant-associated bacterial and fungal environmental communities. Four key findings were revealed. First, analysis of microbial datasets at family level guaranteed both consistency and adequate coverage when using either method. Second, the performance of both methods used are related to community diversity and sample sequencing depth. Third, differences in the method used affected sample diversity and number of detected differentially abundant families upon treatment; this may lead researchers to draw different biological conclusions. Fourth, the observed differences can mostly be attributed to low abundant (relative abundance < 0.1%) families, thus extra care is recommended when studying rare species using metabarcoding. The ASV method used outperformed the adopted OTU method concerning community diversity, especially for fungus-related sequences, but only when the sequencing depth was sufficient to capture the community complexity. Conclusions Investigation of metabarcoding data should be done with care. Correct biological interpretation depends on several factors, including in-depth sequencing of the samples, choice of the most appropriate filtering strategy for the specific research goal, and use of family level for data clustering.

General Unified Microbiome Profiling Pipeline (GUMPP) for Large Scale, Streamlined and Reproducible Analysis of Bacterial 16S rRNA Data to Predicted Microbial Metagenomes, Enzymatic Reactions and Metabolic Pathways

“…These cautionary notes listed above are intended to raise the awareness of the biological caveats of the genus, OUT and ASV levels of analyses for users. From this integrative view of biological influences the genus level analysis fits a more reserved type of analysis with arguably lower resolution, but congruent with an existing microbial taxonomy system in comparison to the ASV level of analysis, whereas OTU represents a compromise [ 14 , 44 ]. By utilizing ASV some genera expand into species and strains that have sufficient diversity within the 16S rRNA and contribute to ASVs, while other genera that contain species and strains with identical 16S rRNA in the region analyzed do not [ 14 , 44 ].…”

“…From this integrative view of biological influences the genus level analysis fits a more reserved type of analysis with arguably lower resolution, but congruent with an existing microbial taxonomy system in comparison to the ASV level of analysis, whereas OTU represents a compromise [ 14 , 44 ]. By utilizing ASV some genera expand into species and strains that have sufficient diversity within the 16S rRNA and contribute to ASVs, while other genera that contain species and strains with identical 16S rRNA in the region analyzed do not [ 14 , 44 ]. This biological distinction between genus, OTU or ASV levels of analysis has potentially large implications for the information forwarded to subsequent data types (functional genes, enzymatic reactions, metabolic pathays) irrespective of program utilized (PICRUSt, Tax4Fun, Piphillin or GUMPP).…”

“…Recent research highlights the risk of splitting a single bacterial genome into separate clusters when ASVs are used to analyze 16S rRNA gene sequence data. Although there is also a risk of clustering ASVs from different species into the same OTU when using broad distance thresholds, those risks are of less concern than artificially splitting a genome into separate ASVs and OTUs [ 14 , 44 ]. Based on the results presented here ( Figure 3 and Figure 4 ), the choice of level of analysis (genus, OTU, ASV) is far from arbitrary and may lead researchers to draw different biological conclusions.…”

“…Based on the results presented here ( Figure 3 and Figure 4 ), the choice of level of analysis (genus, OTU, ASV) is far from arbitrary and may lead researchers to draw different biological conclusions. The work presented in this study highlights the utility of GUMPP that enables researchers to analyze the data at all three levels at the same time, generates functional gene, enzymatic reactions and metabolic pathways datasets for downstream machine learning exploration in relation to human diseases [ 44 ].…”

Understanding the Shift in the Microbiome of Composts That Are Optimized for a Better Fit-for-Purpose in Growing Media

“…Quality of the preprocessed sequences was checked using FastQC version 0.11.8 ( Andrews, 2010 ). Further processing of the sequences was performed using the DADA2 pipeline version 1.12.1 ( Callahan et al, 2015 ), as described in detail in Joos et al (2020) . Reads of a quality score less than or equal to two were truncated (truncQ = 2).…”