RT Journal Article
SR Electronic
T1 Measuring and Mitigating PCR Bias in Microbiome Data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 604025
DO 10.1101/604025
A1 Justin D. Silverman
A1 Rachael J. Bloom
A1 Sharon Jiang
A1 Heather K. Durand
A1 Sayan Mukherjee
A1 Lawrence A. David
YR 2019
UL http://biorxiv.org/content/early/2019/04/09/604025.abstract
AB PCR amplification plays a central role in the measurement of mixed microbial communities via high-throughput sequencing. Yet PCR is also known to be a common source of bias in microbiome data. Here we present a paired modeling and experimental approach to characterize and mitigate PCR bias in microbiome studies. We use experimental data from mock bacterial communities to validate our approach and human gut microbiota samples to characterize PCR bias under real-world conditions. Our results suggest that PCR can bias estimates of microbial relative abundances by a factor of 2-4 but that this bias can be mitigated using simple Bayesian multinomial logistic-normal linear models.Author summary High-throughput sequencing is often used to profile host-associated microbial communities. Many processing steps are required to transform a community of bacteria into a pool of DNA suitable for sequencing. One important step is amplification where, to create enough DNA for sequencing, DNA from many different bacteria are repeatedly copied using a technique called Polymerase Chain Reaction (PCR). However, PCR is known to introduce bias as DNA from some bacteria are more efficiently copied than others. Here we introduce an experimental procedure that allows this bias to be measured and computational techniques that allow this bias to be mitigated in sequencing data.