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.

Toward quantitative metabarcoding

“…There is abundant evidence that the relationship between the true composition of DNA contained in a sample and the reads emerging from the sequencer is far from simple. This fact has been documented in the microbiome and microbial literatures (Gloor et al 2017, McLaren et al 2019, Silverman et al 2021 but less so in the ecological literature (but see e.g., Thomas et al 2016). The most compelling evidence for this phenomenon comes from the analysis of mock communities in which researchers create a known mixture of DNA from a suite of taxa of interest and compare the relative abundance of the reads from each taxa against the known community.…”

“…Read counts following amplification and sequencing invariably fail to match – or often, even approximate – the mock-community DNA starting proportions. For example, relative read counts deviate strongly from the mock communities created for freshwater mussels (Coghlan et al 2021), freshwater invertebrates (Fernández et al 2018), arthropods (Piñol et al 2015, Krehenwinkel et al 2017), freshwater fish (Hänfling et al 2016, Rivera et al 2021), marine vertebrates (Port et al 2016, Andruszkiewicz et al 2017), fungi (Adams et al 2013, De Filippis et al 2017, Palmer et al 2018), diet studies from a range of organisms (Ford et al 2016, Thomas et al 2016, Ando et al 2020, Tournayre et al 2020), and microbiome studies (McLaren et al 2019, Silverman et al 2021). Beyond their obvious taxonomic diversity, these analyses span a wide range of methodological implementations (i.e.…”

Inferring Species Compositions of Complex Fungal Communities from Long- and Short-Read Sequence Data

“…On the other hand, amplification biases are another major source of poor community recovery. These biases come mainly from inconsistent amplification of the barcoding regions of different species, caused by copy number variations and different primer binding specificities ( 53 , 54 ). Also, particularly for fungi, variations in barcode (amplicon) length are the major source of bias in recording fungal community compositions, with longer barcodes being underrepresented ( 55 ).…”

Enrofloxacin Alters Fecal Microbiota and Resistome Irrespective of Its Dose in Calves

“…Comparison of the quantitative changes of the ARGs is more reliable using qPCR than the Hi-C method, because qPCR readings were normalized using the 16S rRNA gene. Nevertheless, the Hi-C method has multiple advantages: it is less prone to bias, since primers are not required [ 63 ]; bacterial taxa associated with ARGs can be identified; it provides insight into the spread of ARGs [ 64 ].…”