Toward quantitative metabarcoding

Abstract

Amplicon-sequence data from environmental DNA (eDNA) and microbiome studies provide important information for ecology, conservation, management, and health. At present, amplicon-sequencing studies-known also as metabarcoding studies, in which the primary data consist of targeted, amplified fragments of DNA sequenced from many taxa in a mixture-struggle to link genetic observations to the underlying biology in a quantitative way, but many applications require quantitative information about the taxa or systems under scrutiny. As metabarcoding studies proliferate in ecology, it becomes more important to develop ways to make them quantitative to ensure that their conclusions are adequately supported. Here we link previously disparate sets of techniques for making such data quantitative, showing that the underlying polymerase chain reaction mechanism explains the observed patterns of amplicon data in a general way. By modeling the process through which amplicon-sequence data arise, rather than transforming the data post hoc, we show how to estimate the starting DNA proportions from a mixture of many taxa. We illustrate how to calibrate the model using mock communities and apply the approach to simulated data and a series of empirical examples. Our approach opens the door to improve the use of metabarcoding data in a wide range of applications in ecology, public health, and related fields.

Distinct latitudinal community patterns of Arctic marine vertebrates along the East Greenlandic coast detected by environmental DNA

“…As we here have no other data source to validate actual dominant species at the sampled sites and can only rely on known species distribution patterns, this remains speculative and thus warrants further investigation. However, meticulous quantification techniques from metabarcoding data are on the horizon, and we expect such inferences to become more reliable going forward (Shelton, Gold, et al, 2022).…”

Towards a framework to unify the relationship between numerical abundance, biomass, and quantitative eDNA

“…It is important to draw a distinction between factors influencing the interpretation of quantitative eDNA data that can affect its distribution in nature, vs. issues associated with molecular methods that can affect the assessment of that quantification. Allometry represents an 'ecology-side' variable impacting the pseudo steady-state distribution of eDNA observed in environments, similar to the impacts of factors such as transportation (17,23,24), temperature (25,26), pH (27), activity (28,29) (35,36), and we are confident that the utility of applying our framework to metabarcoding data will improve as methods progress to improve the fidelity of metabarcoding data to template concentrations. However, we nevertheless suspect that currently applying allometric corrections to metabarcoding datasets will likely be challenging in communities with low species numbers because stochastic primer amplification bias may mask or overwhelm potential allometric effects.…”

“…Metabarcoding methods, however, are likely to generate substantially more 'noise' in the relationship between quantitative eDNA data (i.e., species read counts) and template eDNA concentrations because issues like primer bias (and resulting preferential amplification) as well as community composition, can add substantial 'noise' to the relationship between quantitative metabarcoding eDNA data and species abundance (38)(39)(40). Research is progressing on how to disentangle such effects (41,42), and we are confident that the utility of applying our framework to metabarcoding data will improve as methods progress to improve the fidelity of quantitative metabarcoding data to template eDNA concentration. However, we nevertheless suspect that currently applying allometric corrections to metabarcoding datasets will likely be challenging in communities with low species numbers because stochastic primer amplification bias may mask or overwhelm any potential allometric effects.…”

Advances in Environmental DNA Sampling for Observing Ocean Twilight Zone Animal Diversity

“…Each step of the eDNA analysis process, from experimental design and sample collection to data analysis, impacts the final result (Figure 2). Of the different steps that could introduce bias (Eble et al, 2020;Shelton et al, 2022), sampling approaches and strategies are particularly underexplored (Govindarajan et al, 2022).…”

“…They should be coupled with lab and field experiments for calibration, data analysis, and designing representative and effective field sampling strategies. For inferring biomass of multiple species (e.g., quantitative metabarcoding), incorporating an understanding of PCR dynamics into overall data interpretation will be especially important (Shelton et al, 2022). More research is needed to determine appropriate spatial and temporal sampling scales, which may depend on regional ocean currents; the biology of the target organisms; and specific research questions.…”