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Measuring and mitigating PCR bias in microbiota datasets.

ABSTRACT: 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.

SUBMITTER: Silverman JD

PROVIDER: S-EPMC8284789 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Measuring and mitigating PCR bias in microbiota datasets.

Silverman Justin D JD Bloom Rachael J RJ Jiang Sharon S Durand Heather K HK Dallow Eric E Mukherjee Sayan S David Lawrence A LA

PLoS computational biology 20210706 7

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 hu ...[more]

PMID: 34228723

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