This Genome Watch highlights new computational approaches aimed at circumventing contamination in low-biomass microbiome studies.
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References
Kennedy, K. M. et al. Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies. Nature 613, 639–649 (2023).
Tan, C. C. S. et al. No evidence for a common blood microbiome based on a population study of 9,770 healthy humans. Nat. Microbiol. 8, 973–985 (2023).
Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).
Davis, N. M. et al. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226 (2018).
Austin, G. I. et al. Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01696-w (2023).
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Shao, Y. Scrubbing contaminated microbiomes. Nat Rev Microbiol 21, 554 (2023). https://doi.org/10.1038/s41579-023-00941-y
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DOI: https://doi.org/10.1038/s41579-023-00941-y
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