Efficiently Analysing Large Viral Data Sets in Computational Phylogenomics

Viral evolutionary analyses are confronted with increasingly large sequence data sets, both in terms of sequence length and number of sequences. This can result in considerable computational burden, not only to infer phylogenies but also to obtain associated estimates such as their time scales and phylogeographic patterns. Here, we illustrate two frequently-used approaches to obtain phylogenomic estimates of time-measured trees and spatial dispersal patterns for fast-evolving viruses. First, we discuss computationally efficient procedures that employ a fixed tree topology obtained through maximum likelihood inference to estimate molecular clock rates and phylogeographic spread for Dengue virus genomes. Using the same viral example, we also illustrate Bayesian phylodynamic inference that jointly infers time-measured trees and phylogeo-graphy, including covariates of spatial dispersal, from sequence and trait data. We highlight state-of-the-art efforts to perform such computations more efficiently. Finally, we compare the estimates obtained by both approaches and discuss their strengths and potential pitfalls.