RT Journal Article
SR Electronic
T1 Consistent and correctable bias in metagenomic sequencing experiments
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 559831
DO 10.1101/559831
A1 Michael R. McLaren
A1 Amy D. Willis
A1 Benjamin J. Callahan
YR 2019
UL http://biorxiv.org/content/early/2019/07/13/559831.abstract
AB 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 towards 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 towards truly quantitative and reproducible metagenomics measurements.