Equivolumetric protocol generates library sizes proportional to total microbial load in next-generation sequencing

Abstract

Next-generation sequencing (NGS) has been extensively employed to perform microbiome characterization worldwide. As a culture-independent methodology, it has allowed high-level profiling of sample microbial composition. However, most studies are limited to information regarding relative bacterial abundances, ignoring scenarios in which sample microbe biomass can vary widely. Here, we develop an equivolumetric protocol for amplicon library preparation capable of generating NGS data responsive to input DNA, recovering proportionality between observed read counts and absolute bacterial abundances. Under specified conditions, we argue that the estimation of colony-forming units (CFU), the most common unit of bacterial abundance in classical microbiology, is challenged mostly by resolution and taxon-to-taxon variation. We propose Bayesian cumulative probability models to address such issues. Our results indicate that predictive errors vary consistently below one order of magnitude for observed bacteria. We also demonstrate our approach has the potential to generalize to previously unseen bacteria, but predictive performance is hampered by specific taxa of uncommon profile. Finally, it remains clear that NGS data are not inherently restricted to relative information only, and microbiome science can indeed meet the working scales of traditional microbiology.

One year cross-sectional study in adult and neonatal intensive care units reveals the bacterial and antimicrobial resistance genes profiles in patients and hospital surfaces

“…We performed amplicon sequencing library preparation for bacteria using the V3/V4 16S rRNA gene primers 341F and 806R [26,27] in a two-step equivolumetric PCR protocol [28]. The first PCR was performed with V3/V4 universal primers containing a partial Illumina adaptor, based on TruSeq structure (Illumina, USA) that allows a second PCR with the indexing sequences similar to procedures described previously [29].…”

SQMtools: automated processing and visual analysis of ’omics data with R and anvi’o

“…The debate over the best way to analyse microbiome data is still ongoing (see e.g. two alternative avenues in [17,18]) and older methods such as DESeq2 might still outperform composition-aware methods for some applications (see Methods). In SQMtools, we provide common normalizations that can facilitate data exploration as well as the raw abundance data required to perform more advanced analyses, but we do not promote any particular statistical method as we expect the field to continue evolving.…”

SQMtools: automated processing and visual analysis of ’omics data with R and anvi’o

“…The debate over the best way to analyze microbiome data is still ongoing (see e.g. two alternative avenues in Quinn et al, 2019 andCruz et al, 2020) and older methods such as DESeq2 might still outperform composition-aware methods for some applications (see Methods). In SQMtools, we provide common normalizations that can facilitate data exploration as well as the counting only the reads assigned to the functions in the subset raw abundance data required to perform more advanced analyses, but we do not promote any particular statistical method as we expect the field to continue evolving.…”