TY - JOUR T1 - Consistent and correctable bias in metagenomic sequencing experiments JF - bioRxiv DO - 10.1101/559831 SP - 559831 AU - Michael R. McLaren AU - Amy D. Willis AU - Benjamin J. Callahan Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/07/13/559831.abstract N2 - 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. ER -