Signature-Based Clustering for Analysis of the Wound Microbiome

Chronic wounds present a significant risk to the patient and a substantial drain on health budgets, with the problem likely to worsen markedly with increased incidence of type II diabetes. The wound fluid microbiome is known to influence wound healing outcomes, but is poorly characterised. Next Generation Sequencing approaches yield abundant data from wound samples, but progress in understanding these microbial communities may be hampered by the speed of existing analysis pipelines and limitations on coverage by 16S databases. This paper presents SigClust, a novel clustering method based on binary signatures derived from sequence reads. SigClust yields superior cluster coherence and separation of metagenomic read data in timeframes substantially reduced from those of alternative methods. We demonstrate its utility in the wound context on a preliminary set of labelled patient data. We show how a time course analysis based on tracking the dominant clusters over successive wound samples can identify markers of both successful wound healing and wounds refractory to treatment. Clusters prominent in these analyses are found to correspond to bacterial species known to be implicated as a determinant of wound outcomes, notably a number of strains of Staphylococcus aureus. The clusters obtained rapidly via SigClust support improved understanding of the wound microbiome without direct reliance on a reference database, offering the promise of a SigClust-based pipeline for wound analysis and prediction, and potentially novel methods for wound treatment and management.