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.

本研究开发了一种基于时序采样分子序列估算分歧时间的贝叶斯马尔可夫链蒙特卡洛（Markov chain Monte Carlo, MCMC）算法。该类数据常采集于病毒流行疫情，在古代DNA研究中亦可从不同物种中获取。本研究推导了出生-死亡-时序采样（birth–death-sequential-sampling, BDSS）模型下系统发育树节点的年龄分布，并将其作为定年分析中分歧时间的先验分布。本研究将该先验分布集成至PAML软件包的MCMCtree程序中，用于分歧时间定年。BDSS先验具备极强的灵活性，通过调整不同参数可生成形态差异显著的系统发育树，适用于检验后验时间估计的敏感性。本研究将所提方法应用于两组数据集：其一为SIV/HIV-2基因数据集，并与基于似然的定年方法进行对比；其二为不同宿主来源的流感H1基因数据集，并与贝叶斯分析软件BEAST进行对比。本研究检验了系统发育树拓扑结构对时间估计的影响，并提出定年分析中应避免使用多分支共识树。本研究发现，古老节点的后验时间估计对时间与替换速率的先验分布较为敏感，同时指出此前的贝叶斯定年研究可能得到了过度自信的估计结果。