Consistent and correctable bias in metagenomic sequencing experiments
By Michael R. McLaren, A. Willis, Benjamin J. Callahan. bioRxiv, 2019
384 citations recorded by the source corpus.
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.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/b341b1174ca92ccff912889ba7c419f697d2e512
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Citations in the corpus
- Reporting guidelines for Human Microbiome Research: the STORMS checklist (2021, background): “Many forms of bias, such as residual/unmeasured confounding, bias related to compositional analysis 42 , measurement bias or selection bias 43 , could affect”
- Benchmarking Metagenomics Tools for Taxonomic Classification. (2019, background): “Though use of negative controls has recently improved in metagenomics studies, there is currently no accepted approach for correcting for contamination using these controls (Davis et al., 2018; McLaren et al., 2019).”
- NetCoMi: network construction and comparison for microbiome data in R (2020, methodology): “[6], we refer to marker-gene and metagenomic sequencing techniques as MGS techniques.”