Data from: Dating phylogenies with sequentially sampled tips

We develop a Bayesian Markov chain Monte Carlo (MCMC) algorithm for
estimating divergence times using sequentially sampled molecular
sequences. This type of data is commonly collected during viral epidemics
and is sometimes available from different species in ancient DNA studies.
We derive the distribution of ages of nodes in the tree under a
birth–death-sequential-sampling (BDSS) model and use it as the prior for
divergence times in the dating analysis. We implement the prior in the
MCMCtree program in the PAML package for divergence dating. The BDSS prior
is very flexible and, with different parameters, can generate trees of
very different shapes, suitable for examining the sensitivity of posterior
time estimates. We apply the method to a data set of SIV/HIV-2 genes in
comparison with a likelihood-based dating method, and to a data set of
influenza H1 genes from different hosts in comparison with the Bayesian
program BEAST. We examined the impact of tree topology on time estimates
and suggest that multifurcating consensus trees should be avoided in
dating analysis. We found posterior time estimates for old nodes to be
sensitive to the priors on times and rates and suggest that previous
Bayesian dating studies may have produced overconfident estimates.