Open AccessMeasuring and mitigating PCR bias in microbiota datasets
Author(s) -
Justin D. Silverman,
Rachael J. Bloom,
Sharon Jiang,
Heather K. Durand,
Eric P. Dallow,
Sayan Mukherjee,
Lawrence A. David
Publication year - 2021
Publication title -
plos computational biology/plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1009113
Subject(s) - biology , ribosomal rna , 16s ribosomal rna , microbiome , computational biology , primer (cosmetics) , genetics , polymerase chain reaction , gene , chemistry , organic chemistry
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.