Toward quantitative metabarcoding

Abstract

Amplicon-sequence data from environmental DNA (eDNA) and microbiome studies provides 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 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 following decades of microbial and microbiome work using similar techniques, it becomes more important to develop ways ot 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 PCR mechanism explains observed patterns of amplicon data in a general way. By modeling the process through which amplicon-sequence data arises, 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.

Message in a Bottle: Archived DNA Reveals Marine Heatwave-Associated Shifts in Fish Assemblages

“…We note this model assumes that PCR amplification has not approached saturation and therefore the PCR is still amplifying exponentially. We, and others (McLaren et al, 2019), argue this assumption is valid because 1) the total concentration of DNA within a filtered ethanol sample is low (<1 ng/μL), 2) the PCR reagents are supplied in excess and therefore are unlikely to be saturating the PCR, and 3) evidence from previous studies supports these assumptions (McLaren et al, 2019; Shelton et al, 2022; Silverman et al, 2021). However, future models could be developed to account for a saturating PCR curve (Lalam, 2006).…”

“…We estimated the abundance of ichthyoplankton in each jar using a novel joint Bayesian hierarchical model described in Shelton et al (2022) and also detailed here. We first model taxon sequence-read counts from metabarcoding to account for the PCR process, in which each taxon is subject to a different amplification efficiency based on the primer set used.…”

“…the PCR reagents are supplied in excess and therefore are unlikely to be saturating the PCR, and 3) evidence from previous studies supports these assumptions (McLaren et al, 2019;Shelton et al, 2022;Silverman et al, 2021). However, future models could be developed to account for a saturating PCR curve (Lalam, 2006).…”

“…Including morphological count data enables us to estimate the confounded parameters by bounding additional information about the underlying species abundances. Below we discuss how these two datasets are integrated (see Shelton et al (2022) for the application of mock community data to similarly calibrate metabarcoding data).…”

Extracting abundance information from DNA-based data

“…Note that because q/ddPCR is carried out after many of the wet- lab steps have been carried out, multiview modelling does not remove pipeline noise, and a spike-in is still needed to achieve within-species quantification. Shelton et al (2022a) advocate a similar approach but via the construction of a ‘mock community’ containing tissue of all species of interest and subjecting it to the same PCR protocol as the samples. From this mock community, species-specific PCR biases are calculated and used to extract across-species abundance information.…”