Rapid analysis of metagenomic data using signature-based clustering

• Published in: BMC bioinformatics Vol. 19; no. Suppl 20; pp. 509 - 93
• Main Authors: Chappell, Timothy, Geva, Shlomo, Hogan, James M., Huygens, Flavia, Rathnayake, Irani U., Rudd, Stephen, Kelly, Wayne, Perrin, Dimitri
• Format: Journal Article
• Language: English
• Published: London BioMed Central 21.12.2018; BioMed Central Ltd; BMC
• Subjects: Algorithms; Alternation of generations; Biofilms; Bioinformatics; Biomedical and Life Sciences; Cluster Analysis; Clustering; Communities; Community analysis; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Data Analysis; Data processing; Datasets; Deoxyribonucleic acid; DNA; DNA sequencing; Elderly; Genetic transformation; Health aspects; Humans; Life Sciences; Metagenomics; Metagenomics - methods; Methods; Microarrays; Microbial activity; Microbiomes; Microbiota - genetics; Microorganisms; Next-generation sequencing; Patients; Physiological aspects; Read signatures; Signatures; Staphylococcus aureus; Taxonomy; Ulcers; Wound healing; Metagenomics; Community analysis; Wound healing; Read signatures; Clustering
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-018-2540-4

Background Sequencing highly-variable 16S regions is a common and often effective approach to the study of microbial communities, and next-generation sequencing (NGS) technologies provide abundant quantities of data for analysis. However, the speed of existing analysis pipelines may limit our ability to work with these quantities of data. Furthermore, the limited coverage of existing 16S databases may hamper our ability to characterise these communities, particularly in the context of complex or poorly studied environments. Results In this article we present the SigClust algorithm, a novel clustering method involving the transformation of sequence reads into binary signatures. When compared to other published methods, SigClust yields superior cluster coherence and separation of metagenomic read data, while operating within substantially reduced timeframes. We demonstrate its utility on published Illumina datasets and on a large collection of labelled wound reads sourced from patients in a wound clinic. The temporal analysis is based on tracking the dominant clusters of wound samples over time. The analysis can identify markers of both healing and non-healing wounds in response to treatment. Prominent clusters are found, corresponding to bacterial species known to be associated with unfavourable healing outcomes, including a number of strains of Staphylococcus aureus . Conclusions SigClust identifies clusters rapidly and supports an improved understanding of the wound microbiome without reliance on a reference database. The results indicate a promising use for a SigClust -based pipeline in wound analysis and prediction, and a possible novel method for wound management and treatment.