DM-PhyClus: a Bayesian phylogenetic algorithm for infectious disease transmission cluster inference.

ABSTRACT:

Background

Conventional phylogenetic clustering approaches rely on arbitrary cutpoints applied a posteriori to phylogenetic estimates. Although in practice, Bayesian and bootstrap-based clustering tend to lead to similar estimates, they often produce conflicting measures of confidence in clusters. The current study proposes a new Bayesian phylogenetic clustering algorithm, which we refer to as DM-PhyClus (Dirichlet-Multinomial Phylogenetic Clustering), that identifies sets of sequences resulting from quick transmission chains, thus yielding easily-interpretable clusters, without using any ad hoc distance or confidence requirement.

Results

Simulations reveal that DM-PhyClus can outperform conventional clustering methods, as well as the Gap procedure, a pure distance-based alg

SUBMITTER: Villandre L 

PROVIDER: S-EPMC6137936 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature