Measuring and mitigating PCR bias in microbiota datasets

Justin D. Silverman, Rachael J. Bloom, Sharon Jiang, Heather K. Durand, Eric Dallow, Sayan Mukherjee, Lawrence A. David

    Research output: Contribution to journalArticlepeer-review

    7 Scopus citations


    PCR amplification plays an integral role in the measurement of mixed microbial communities via high-throughput DNA sequencing of the 16S ribosomal RNA (rRNA) gene. Yet PCR is also known to introduce multiple forms of bias in 16S rRNA studies. Here we present a paired modeling and experimental approach to characterize and mitigate PCR NPM-bias (PCR bias from non-primer-mismatch sources) in microbiota surveys. We use experimental data from mock bacterial communities to validate our approach and human gut microbiota samples to characterize PCR NPM-bias under real-world conditions. Our results suggest that PCR NPM-bias can skew estimates of microbial relative abundances by a factor of 4 or more, but that this bias can be mitigated using log-ratio linear models.

    Original languageEnglish (US)
    Article numbere1009113
    JournalPLoS computational biology
    Issue number7
    StatePublished - Jul 2021

    All Science Journal Classification (ASJC) codes

    • Ecology, Evolution, Behavior and Systematics
    • Ecology
    • Modeling and Simulation
    • Molecular Biology
    • Genetics
    • Cellular and Molecular Neuroscience
    • Computational Theory and Mathematics


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