The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Consistent and correctable bias in metagenomic sequencing experiments
Open accessAbstract
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.
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Identifying biases and their potential solutions in human microbiome studies
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Quantifying bias introduced by sample collection in relative and absolute microbiome measurements
Maghini, D. G., Dvorak, M., Dahlen, A., Roos, M., Doyle, B., Kuersten, S., & Bhatt, A. S. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01754-3 · Cited by 116
Metadata
- Authors
- McLaren, M. R., Willis, A. D., & Callahan, B. J.
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2019-09-10
- Volume
- 8
- ISSN
- 2050-084X (ISSN-L)
- OpenAlex
- W2972720814
- Cited by
- 429
- References
- 64
- FWCI
- 14.32
- Type
- journal article
- Language
- en
- Open access
- gold
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