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. 2018 Dec 21;19(Suppl 20):509.
doi: 10.1186/s12859-018-2540-4.

Rapid analysis of metagenomic data using signature-based clustering

Timothy Chappell  1 , Shlomo Geva  1 , James M Hogan  1 , Flavia Huygens  2   3 , Irani U Rathnayake  2   3 , Stephen Rudd  4 , Wayne Kelly  1 , Dimitri Perrin  5
Affiliations

Rapid analysis of metagenomic data using signature-based clustering

Timothy Chappell et al. BMC Bioinformatics. .

Abstract

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.

Keywords: Clustering; Community analysis; Metagenomics; Read signatures; Wound healing.

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Conflict of interest statement

Ethics approval and consent to participate

Collection of wound samples was approved by QUT’s Human Research Ethics Committee (approval number: 1000001255). The authors would like to thank Michell Gibb and Christina Parker for wound sample and patient data collection, and are very grateful to all the participants in the study for agreeing to take part in this research.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Clustering and analysis pipeline
Fig. 2
Fig. 2
Needleman-Wunch global alignment cluster analysis. Histogram of Needleman-Wunch scores between random pairs of reads in the same cluster (intracluster pairs) and pairs of reads from different clusters (intercluster pairs)
Fig. 3
Fig. 3
Smith-Waterman global alignment cluster analysis. Histogram of Smith-Waterman scores between random pairs of reads in the same cluster (intracluster pairs) and pairs of reads from different clusters (intercluster pairs)
Fig. 4
Fig. 4
Relative Cluster Abundance for wound #4059
Fig. 5
Fig. 5
Bray Curtis dissimilarity analysis for wound #4059. Each series shows the variation in BC dissimilarity for each time point relative to the observation immediately before, commencing with the time point following the label. So, label W4 shows observations for W5 – relative to W4, for W6 – relative to W5, and so on. For W11, we see only the single observation at W12, relative to W11
Fig. 6
Fig. 6
Relative Cluster Abundance for wound #4032
Fig. 7
Fig. 7
Bray Curtis dissimilarity analysis for wound #4032. Each series shows the variation in BC dissimilarity for each time point relative to the observation immediately before, commencing with the time point following the label. See the caption for Fig. 5 for a more detailed explanation
Fig. 8
Fig. 8
Relative Cluster Abundance for wound #4068
Fig. 9
Fig. 9
Relative Cluster Abundance for wound #4046
See this image and copyright information in PMC

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