Strain resolution and wide relative abundance quantification of bacterial microbiomes with nanopore sequencing of the ribosomal RNA operon

Abstract

Identification and quantification of microbiomes at strain level taxonomic resolution is increasingly sought. Longread sequencing of sufficiently high accuracy can provide this using ribosomal RNA operon (rrn) sequencing, subject to strains differing by at least one base in some of their operons. Prior studies of nanopore-sequenced amplicon reads have not established reliable strain level resolution and sensitive quantification. Here we demonstrate that if a sufficient number of rrn operon reads with a mean error rate of better than about 0.008 errors per base are available in a dataset, then strain identification and quantification, mediated by operon-level resolution, can often be achieved. Two multiple dataset sources of real, nanopore-sequenced bacterial mock microbiome were accessed, and multiple datasets of a realistic mock human gut microbiome, using state-of-the-art simulated nanopore-sequenced reads, were simulated. These datasets had read quality distributions lying between those of the real datasets, did not have multiple strains for any species, but, for the higher quality datasets, allowed demonstration of within-strain operon resolution and quantification over 2 orders of magnitude. The simulation-generated datasets demonstrated multiple cases of within-species strain resolution with accurate relative abundance quantification. The analysis of such datasets has been implemented here in a pipeline of 4 multi-parameter command line calls. This allows intra-species resolution and quantification over at least 3 orders of magnitude with more than 50 strains present in the microbiome