Current methods to characterize microbial communities generally employ sequencing of the 16S rRNA gene (<500 bp) with high accuracy (∼99%) but limited phylogenetic resolution. However, long-read sequencing now allows for the profiling of near-full-length ribosomal operons (16S-ITS-23S rRNA genes) on platforms such as the Oxford Nanopore MinION. Here, we describe an rRNA operon database with >300 ,000 entries, representing >10 ,000 prokaryotic species and ∼ 150, 000 strains. Additionally, BLAST parameters were identified for strain-level resolution using in silico mutated, mock rRNA operon sequences (70-95% identity) from four bacterial phyla and two members of the Euryarchaeota, mimicking MinION reads. MegaBLAST settings were determined that required <3 s per read on a Mac Mini with strain-level resolution for sequences with >84% identity. These settings were tested on rRNA operon libraries from the human respiratory tract, farm/forest soils and marine sponges ( n = 1, 322, 818 reads for all sample sets). Most rRNA operon reads in this data set yielded best BLAST hits (95 ± 8%). However, only 38-82% of library reads were compatible with strain-level resolution, reflecting the dominance of human/biomedical-associated prokaryotic entries in the database. Since the MinION and the Mac Mini are both portable, this study demonstrates the possibility of rapid strain-level microbiome analysis in the field.
Keywords: MegaBLAST screening; MinION sequencing; portable sequence analysis; rRNA operon database; ribosomal RNA operon profiling; strain-level microbiome analysis.
Published by Oxford University Press on behalf of FEMS 2022.