Consistent and correctable bias in metagenomic sequencing experiments

Author:

McLaren Michael R1ORCID,Willis Amy D2ORCID,Callahan Benjamin J13ORCID

Affiliation:

1. Department of Population Health and Pathobiology, North Carolina State University, Raleigh, United States

2. Department of Biostatistics, University of Washington, Seattle, United States

3. Bioinformatics Research Center, North Carolina State University, Raleigh, United States

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.

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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