Abstract
Summary Highly-used methods for identifying transmission clusters of rapidly-evolving pathogens from molecular data require a user-determined distance threshold. The choice of threshold is often motivated by epidemiological information known a priori, which may be unfeasible for epidemics without rich epidemiological information. TreeN93 is a fully non-parametric distance-based method for transmission cluster identification that scales polynomially.
Availability and implementation TreeN93 is implemented in Python 3 and is freely available at https://github.com/niemasd/TreeN93/.
Contact niemamoshiri{at}gmail.com
Copyright
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
Posted August 02, 2018.
TreeN93: a non-parametric distance-based method for inferring viral transmission clusters
