Rapid analysis of metagenomic data using signature-based clustering

被引:2
作者
Chappell, Timothy [1 ]
Geva, Shlomo [1 ]
Hogan, James M. [1 ]
Huygens, Flavia [2 ,3 ]
Rathnayake, Irani U. [2 ,3 ]
Rudd, Stephen [4 ]
Kelly, Wayne [1 ]
Perrin, Dimitri [1 ]
机构
[1] Queensland Univ Technol, Sch Elect Engn & Comp Sci, 2 George St, Brisbane, Qld 4001, Australia
[2] Queensland Univ Technol, Inst Hlth & Biomed Innovat, 60 Musk Ave, Kelvin Grove, Qld 4059, Australia
[3] Queensland Univ Technol, Fac Hlth, Sch Biomed Sci, 2 George St, Brisbane, Qld 4001, Australia
[4] QFAB, Level 6 QBP Bld 80,Chancellors Pl, Brisbane, Qld 4072, Australia
来源
BMC BIOINFORMATICS | 2018年 / 19卷
关键词
Metagenomics; Clustering; Community analysis; Read signatures; Wound healing; SEARCH; MICROBIOLOGY; BACTERIAL;
D O I
10.1186/s12859-018-2540-4
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
BackgroundSequencing 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.ResultsIn 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.ConclusionsSigClust 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.
引用
收藏
页数:15
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