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Automated analysis of phylogenetic clusters

Manon Ragonnet-Cronin1*, Emma Hodcroft1, Stéphane Hué2, Esther Fearnhill3, Valerie Delpech4, Andrew J Leigh Brown1, Samantha Lycett1 and on behalf of the UK HIV Drug Resistance Database

Author Affiliations

1 University of Edinburgh, Edinburgh, UK

2 University College London, London, UK

3 MRC Clinical Trials Unit, London, UK

4 Public Health England, London, UK

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BMC Bioinformatics 2013, 14:317  doi:10.1186/1471-2105-14-317

Published: 6 November 2013



As sequence data sets used for the investigation of pathogen transmission patterns increase in size, automated tools and standardized methods for cluster analysis have become necessary. We have developed an automated Cluster Picker which identifies monophyletic clades meeting user-input criteria for bootstrap support and maximum genetic distance within large phylogenetic trees. A second tool, the Cluster Matcher, automates the process of linking genetic data to epidemiological or clinical data, and matches clusters between runs of the Cluster Picker.


We explore the effect of different bootstrap and genetic distance thresholds on clusters identified in a data set of publicly available HIV sequences, and compare these results to those of a previously published tool for cluster identification. To demonstrate their utility, we then use the Cluster Picker and Cluster Matcher together to investigate how clusters in the data set changed over time. We find that clusters containing sequences from more than one UK location at the first time point (multiple origin) were significantly more likely to grow than those representing only a single location.


The Cluster Picker and Cluster Matcher can rapidly process phylogenetic trees containing tens of thousands of sequences. Together these tools will facilitate comparisons of pathogen transmission dynamics between studies and countries.

Phylogenetics; Cluster; Sequence analysis; Virus; HIV; Epidemiology