Abstract
Marker-gene and metagenomic sequencing have profoundly expanded our ability to measure biological communities. But the measurements they provide differ from the truth, often dramatically, because these experiments are biased toward detecting some taxa over others. This experimental bias makes the taxon or gene abundances measured by different protocols quantitatively incomparable and can lead to spurious biological conclusions. We propose a mathematical model for how bias distorts community measurements based on the properties of real experiments. We validate this model with 16S rRNA gene and shotgun metagenomics data from defined bacterial communities. Our model better fits the experimental data despite being simpler than previous models. We illustrate how our model can be used to evaluate protocols, to understand the effect of bias on downstream statistical analyses, and to measure and correct bias given suitable calibration controls. These results illuminate new avenues toward truly quantitative and reproducible metagenomics measurements.
Highlights
IntroductionMarker-gene and metagenomic sequencing (jointly, MGS) have transformed the study of biological communities
Marker-gene and metagenomic sequencing have transformed the study of biological communities
A mathematical model of MGS bias Consider a marker-gene or metagenomic sequencing (MGS) experiment as a multi-step transformation that takes as input biological material and provides as output the taxonomic profile corresponding to each sample—i.e., the set of measured taxa and their associated relative abundances (Figure 1A)
Summary
Marker-gene and metagenomic sequencing (jointly, MGS) have transformed the study of biological communities. MGS measurements of microbial communities are yielding fundamental new insights into the structure and dynamics of microbial ecosystems and the roles of microbes as drivers of host and ecosystem health (Zeevi et al, 2015; Graham et al, 2016; Knight and Sogin, 2017; Callahan et al, 2017; Lehman et al, 2015). Applications of MGS, often under the alternative terms eDNA sequencing or metabarcoding, increasingly extend beyond microbes to the measurement and monitoring of plants, insects, and vertebrates (Bell et al, 2019; Krehenwinkel et al, 2017; Thomas et al, 2016). The community compositions measured by MGS are wrong
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