Efficient Bayesian phylogenomic dating with new models of trait evolution and rich diversities of living and fossil species

随着物种的分化，它们在基因组中以近似恒定的速度积累核苷酸替换。因此，替代作为时钟推断物种的分歧。通过结合化石记录的信息，推断的物种形成时间可以校准到地质时间。这种方法被称为分子钟测年，在进化生物学中有广泛的应用，例如研究病毒大流行的传播时间，动物和植物多样性的古老速率，物种进化与过去气候或灭绝事件的关系，人类进化，或农业和动物驯化的起源。事实上，进化时间树比没有时间信息的树提供了更丰富的物种历史信息，从而允许制定和测试进化timescales.Currently的假设，贝叶斯方法是最先进的分子钟测年，因为它们允许灵活的建模的进化过程和整合化石的不确定性的分析。贝叶斯时钟测年的进展包括谱系之间的速率变化的随机模型（所谓的放松时钟模型），现存和灭绝类群的性状进化建模，以及“软边界”和灵活的化石校准密度的发展。虽然这些进步使得贝叶斯方法对时钟测年有吸引力，但贝叶斯计算依赖于MCMC采样，这需要计算昂贵的随机模拟，排除了贝叶斯方法用于分析大规模数据集。这是不幸的，因为大规模的分子数据集现在是司空见惯的：几个高通量基因组测序项目现在已经宣布或正在进行中，我们预计将有数千个物种的大量基因组规模的数据（例如，10 K动物基因组和英国的66 K真核生物基因组项目）。随着基因组数据的大量涌现，基于比较解剖学计算革命的形态学数据集数量呈爆炸性增长--X射线断层扫描和摄影测量的广泛应用产生了巨大的性状数据库：MorphoBank和Phenome 10 K现在存储了7,000多个物种的64,200多个表面扫描。现在迫切需要能够利用这些新生成的数据集的计算工具。例如，用目前的方法，推断一个66 K物种的树将需要至少55年的计算时间（从我们以前的一些分析中推断）。显然，分析方法的效率并没有跟上可用数据的数量，并且越来越需要解决进化生物学中的大规模问题。在这个项目中，我们将克服两个主要的挑战，在贝叶斯时钟定年的物种分歧：（i）混合和MCMC算法在分析大型数据集的计算限制，（ii）的限制，目前的性状模型的进化树推理。我们将利用MCMC算法设计的新思想设计新的MCMC算法来提高混合效率，并通过代码改进和并行化来提高计算效率。我们将采用先进的性状模型来推断现存物种和化石物种的时间树。特别是，我们将调整性状模型来分析大型基因组性状数据集，如RNA-seq表达数据。新开发的算法将在我们的MCMCtree软件中实现，并应用于几个大规模的经验数据集与密集采样的现存物种和化石物种。数据分析将为方法开发提供重要的动力，并通过解决进化生物学中的基本问题来展示我们的新软件。我们的提案涉及BBSRC的“数据驱动生物学”和“生物科学的系统方法”的战略重点。

As species diverge, they accumulate nucleotide substitutions in their genomes at a rate approximately constant in time. Thus, substitutions serve as timepieces to infer species divergences. By incorporating information from the fossil record, the inferred speciation timings can be calibrated to geological time. This method, known as molecular-clock dating, has broad applications in evolutionary biology, such as studying the timing of spread of viral pandemics, ancient rates of diversification in animals and plants, the relationship of species evolution with past climate or extinction events, human evolution, or the origin of agriculture and animal domestication. Indeed, evolutionary timetrees provide much richer information about species histories than trees without temporal information, thus allowing the formulation and testing of hypotheses on evolutionary timescales.Currently, Bayesian methods are the-state-of-the-art in molecular-clock dating as they allow flexible modelling of evolutionary processes and integration of fossil uncertainties in the analysis. Progresses in Bayesian clock-dating include stochastic models of rate variation among lineages (so-called relaxed clock models), modelling of trait evolution in extant and extinct taxa, and development of "soft-bounds" and flexible fossil calibration densities. While these advances have made the Bayesian method attractive for clock-dating, Bayesian computation relies on MCMC sampling which requires computationally expensive stochastic simulation, precluding the Bayesian method for analysis of large-scale datasets. This is unfortunate since large scale molecular datasets are now commonplace: several high-throughput genome sequencing projects have now been announced or are in progress and we expect a flood of genome-scale data for several thousand species (e.g. the 10K animal genomes and the UK's 66K eukaryotic genomes projects). This deluge of genome data has been accompanied by an explosive increase in the number of morphological datasets based on a computational revolution in comparative anatomy - the widespread deployment of X-Ray Tomography and photogrammetry resulting in vast databases of trait data: MorphoBank and Phenome10K now store over 64,200 surface scans for over 7,000 species. Computational tools capable of exploiting these newly generated datasets are now urgently required. For example, with current methods, inference of a 66K-species timetree would require at least 55 years of computing time (extrapolating from some of our previous analyses). Evidently, the efficiency of analytic methods has not kept apace with the volume of data available and increasingly required to tackle large scale questions in evolutionary biology. In this project we will overcome two major challenges in Bayesian clock dating of species divergences: (i) the mixing and computational limitations of MCMC algorithms in analyses of large datasets, and (ii) the limitations of current trait models of evolution in timetree inference. We will design novel MCMC algorithms to improve the mixing efficiency making use of new ideas about MCMC algorithm design and improve the computational efficiency through code improvement and parallelization. We will incorporate advanced trait models to infer timetrees of extant and fossil species. In particular, we will adapt trait models to analyse large genomic trait datasets such as RNA-seq expression data. The newly developed algorithms will be implemented in our MCMCtree software, and applied to several large-scale empirical datasets with densely sampled extant and fossil species. The data analyses will provide important motivations for method development and serve to showcase our new software by addressing fundamental questions in evolutionary biology. Our proposal addresses the BBSRC's strategic priorities of "data driven biology" and "system approaches to the biosciences".