期刊
VIRUS EVOLUTION
卷 5, 期 2, 页码 -出版社
OXFORD UNIV PRESS
DOI: 10.1093/ve/vez039
关键词
phylogenetic clustering; molecular epidemiology; transmission
类别
资金
- Bioinformatics Institute (A*STAR)
- NUS Graduate School for Integrative Sciences and Engineering from the National University of Singapore
- Gates Cambridge Trust [OPP1144]
- A*STAR HEIDI programme [H1699f0013]
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 pathogens 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.
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