Background: Despite extensive study, there is no clear consensus on what constitutes a healthy or unhealthy sinus microbiome. The literature includes ~70 microbiome studies using 16S rRNA sequencing. However, the increase in genomic-based microbiome study has bred greater discrepancies in results. Our lab demonstrated these discrepancies arise from 16S rRNA PCR amplification bias: metagenomic sequencing was the only reliable sequencing workflow in the sinuses. In this paper, we apply this method for the first time. Aims: To use metagenomic sequencing to characterise dysbiosis in chronic rhinosinusitis (CRS). Methods: Swabs and paired tissue samples were collected under endoscopic guidance from the middle meatus of 52 patients (16 controls; 15 CRSsNP; 21 CRSwNP). DNA was extracted from swabs (DNeasy Blood&Tissue Kit), and libraries were prepared using native barcoding (SQK-NBK-24, ONT) and sequenced on the nanopore PromethION with adaptive sampling to deplete human sequences. FastQ files were analysed using a custom pipeline to quality filter and further deplete reads mapped to the human reference using minimap2. Taxonomic profiles were generated using Sourmash and a custom database. These microbiome profiles were integrated with patient metadata, including disease status, severity, and inflammatory profiles. Results: Clustering on beta diversity revealed three microbiotypes significantly associated with CRS disease (P0.045) and mucosal Bcell infiltration (P0.008). Type1 mainly consisted of Corynebacterium spp. and C. acnes; 89% of these patients were controls or had low Bcell infiltration. Type2 was dominated by Staph spp.; 83% of these patients had CRS. Type3 represented a heterogeneous group of microbiomes dominated by a single pathogen; 80% of these patients had CRS, and 100% had severe Bcell infiltration. Conclusion: Metagenomic sequencing reveals distinct microbiotypes in the sinus microbiome, significantly associated with CRS disease and mucosal inflammation.