A novel methodology for large-scale phylogeny partition

Prosperi, Mattia C.F.; Ciccozzi, Massimo; Fanti, luri; Saladini, Francesco; Pecorari, Monica; Borghi, Vanni; Di Giambenedetto, Simona; Bruzzone, Bianca; Capetti, Amedeo; Vivarelli, Angela; Rusconi, Stefano; Carla Re, Maria; Gismondo, Maria Rita; Sighinolfi, Laura; Gray, Rebecca R.; Salemi, Marco; Zazzi, Maurizio; De Luca, Andrea

Understanding the determinants of virus transmission is a fundamental step for effective design of screening and intervention strategies to control viral epidemics. Phylogenetic analysis can be a valid approach for the identification of transmission chains, and very-large data sets can be analysed through parallel computation. Here we propose and validate a new methodology for the partition of large-scale phylogenies and the inference of transmission clusters. This approach, on the basis of a depth-first search algorithm, conjugates the evaluation of node reliability, tree topology and patristic distance analysis. The method has been applied to identify transmission clusters of a phylogeny of 11,541 human immunodeficiency virus-1 subtype B pol gene sequences from a large Italian cohort. Molecular transmission chains were characterized by means of different clinical/demographic factors, such as the interaction between male homosexuals and male heterosexuals. Our method takes an advantage of a flexible notion of transmission cluster and can become a general framework to analyse other epidemics.