Inferring putative transmission clusters with Phydelity

Inferring putative transmission clusters with Phydelity
Inferring putative transmission clusters with Phydelity
Other Titles:
Virus Evolution
Publication Date:
09 October 2019
Alvin X Han, Edyth Parker, Sebastian Maurer-Stroh, Colin A Russell, Inferring putative transmission clusters with Phydelity, Virus Evolution, Volume 5, Issue 2, July 2019, vez039,
Current phylogenetic clustering approaches for identifying pathogen transmission clusters are limited by their dependency on arbitrarily defined genetic distance thresholds for within-cluster divergence. Incomplete knowledge of a pathogen’s underlying dynamics often reduces the choice of distance threshold to an exploratory, ad hoc exercise that is difficult to standardise across studies. Phydelity is a new tool for the identification of transmission clusters in pathogen phylogenies. It identifies groups of sequences that are more closely related than the ensemble distribution of the phylogeny under a statistically principled and phylogeny-informed framework, without the introduction of arbitrary distance thresholds. Relative to other distance threshold- and model-based methods, Phydelity outputs clusters with higher purity and lower probability of misclassification in simulated phylogenies. Applying Phydelity to empirical datasets of hepatitis B and C virus infections showed that Phydelity identified clusters with better correspondence to individuals that are more likely to be linked by transmission events relative to other widely used non-parametric phylogenetic clustering methods without the need for parameter calibration. Phydelity is generalisable to any pathogen and can be used to identify putative direct transmission events. Phydelity is freely available at
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Funding Info:
A.X.H. was supported by the A*STAR Graduate Scholarship programme from A*STAR to carry out his PhD work via collaboration between Bioinformatics Institute (A*STAR) and NUS Graduate School for Integrative Sciences and Engineering from the National University of Singapore. E.P. was funded by the Gates Cambridge Trust (Grant number: OPP1144). S.M.S. was supported by the A*STAR HEIDI programme (Grant number: H1699f0013) and Bioinformatics Institute (A*STAR).
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