贝叶斯方法是综合分子序列信息和化石信息来进行物种分化时间估计的强有力的工具。随着基因组数据的爆炸性增长，科学家们需要使用多位点数据进行物种分化时间的估计。但是目前常用的物种分化时间估计的模型和算法尚存在严重问题。本项目将在贝叶斯框架下，研究在宽松分子钟模型下使用化石信息来分析基因组数据以估计物种分化时间的方法和模型。通过研究核苷酸替代速率先验和化石不确定性对后验时间估计的影响，来寻求更加合理的先验设置方法；通过研究数据划分策略对后验时间估计值的影响来评估不同的划分策略，探索最高效的使用基因组数据的方法；建立形态性状进化的模型，联合分析分子和形态数据以估计物种分化时间。通过以上研究改进和完善MCMCTree软件，为用户提供准确、高效、可靠、可用于分析大规模基因组数据的物种分化时间估计软件。

Bayesian methods provide a powerful framework to estimate species divergence times by combining information from molecular sequences with information from the fossil record. With the explosive increase of genomic data, divergence time estimation increasingly uses data of multiple loci (genes or site partitions). Widely used computer programs to estimate divergence times involve a number of inadequacies.that may lead to poor time estimates. In this project, we will study Bayesian methods and models for estimating divergence times under relaxed-clock models which integrate genomics data with fossil information. By investigating the impact of the rate prior and the uncertainty of fossil calibrations, we will identify reasonable rate prior so that the posterior time estimation is robust to prior misspecification. By studying the effect of partitioning and evaluating different partition strategies, we will find the efficient way of partitioning genome data. We will also develop models of morphological trait evolution which will allow us to incorporate morphological and molecular data in a combined analysis,.leading to accurate posterior time estimation. The new models and methods will be implemented in the computer program MCMCTree.