Efficient computational technologies to resolve the Timetree of Life: from ancient DNA to species-rich phylogenies

高效计算技术解析生命时间树：从古代 DNA 到物种丰富的系统发育

• 批准号: BB/Y003624/1
• 负责人: Mario Jose Dos Reis Barros
• 金额: $ 58.65万
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FY003624%2F1
• 关键词: Efficient; computational; technologies; resolve; Timetree

The genomes of organisms accumulate changes with the passage of time, recording the evolutionary history of species divergences and extinctions as a timepiece does. By sequencing genomes and integrating information from fossil records, scientists can reconstruct evolutionary trees and calibrate them to geological time. This molecular-clock dating approach has led to spectacular results by leveraging information from sequenced genomes, greatly enriching our understanding of the macroevolutionary process of species diversifications through time, and the possible driving factors such as major geological events and paleoclimate changes.Bayesian inference provides a flexible framework for integrating various sources of uncertainty in molecular clock dating analyses, such as uncertainties in the rate of molecular evolution over time, in the age and placement of fossils on species phylogenies, and in estimated molecular branch lengths from genomic data. However, Bayesian methods are computationally expensive as they require stochastic MCMC sampling to integrate over the uncertainties, and thus they are unable to handle the deluge of genomic data. This computational limitation is a major problem because genome-scale datasets are needed to achieve high precision in estimated evolutionary timescales, which allows the testing of precise evolutionary hypotheses that could never be addressed with the crude time estimates obtained with small molecular datasets.Our team has a track record of successfully developing Bayesian methods to infer evolutionary timescales, with our software packages, BPP and MCMCtree, being widely used by academic beneficiaries to reconstruct such timescales. The overarching aim of this proposal is to develop new models and algorithms for efficient inference of evolutionary timescales using large genome-scale datasets, and integrate these new methods into our existing software. We will develop (1) models that account for sequence errors (to accommodate DNA degradation) and integrate dated samples for analysis of ancient genomes, (2) cross-bracing calibrations to leverage information in gene duplications and horizontal gene transfer events for inferring ancient timescales, and (3) efficient MCMC sampling algorithms for analysis of species-rich phylogenomic datasets. The improved algorithms will make it possible to analyse large datasets with thousands of genomes, from the Darwin Tree of Life and Earth BioGenome projects, as well as helping reduce the carbon footprint of computational analyses.Finally, we will use our newly developed technologies to tackle three important case studies: (1) The evolution of human populations and other hominids, by integrating analysis of ancient and modern genomes within the multi-species coalescent model with introgression, (2) inferring the species-rich Tree of Life of Tetrapods (mammals, birds, crocodiles, turtles, lepidosaurs, and amphibians) in the context of three major past extinction events (end-Permian, Triassic-Jurassic, and end-Cretaceous), and the rapid global warming event during the Eocene-Palaeocene Thermal Maximum, and (3) inferring the Tree of Life of Eukaryotes, by integrating information from ancient gene duplication and horizontal gene transfer events, which will help reduce uncertainty in the timing some of the deepest speciation events in the history of Life on Earth.

生物体的基因组随着时间的推移不断积累变化，像钟表一样记录着物种分化和进化的历史。通过基因组测序和整合化石记录中的信息，科学家可以重建进化树，并将其校准到地质时间。这种分子钟测年方法通过利用测序基因组的信息而产生了惊人的结果，极大地丰富了我们对物种多样性随时间变化的宏观进化过程以及可能的驱动因素（如重大地质事件和古气候变化）的理解。贝叶斯推断为整合分子钟测年分析中的各种不确定性来源提供了一个灵活的框架，例如分子进化速率随时间的不确定性，化石在物种进化中的年龄和位置，以及从基因组数据估计的分子分支长度。然而，贝叶斯方法是计算昂贵的，因为它们需要随机MCMC采样来整合不确定性，因此它们无法处理洪水般的基因组数据。这种计算限制是一个主要问题，因为需要基因组规模的数据集来实现估计进化时间尺度的高精度，这允许测试精确的进化假设，而这些假设永远无法用小分子数据集获得的粗略时间估计来解决。我们的团队有成功开发贝叶斯方法来推断进化时间尺度的记录，我们的软件包BPP和MCMCTree，被学术界的受益者广泛用于重建这样的时间尺度。该提案的总体目标是开发新的模型和算法，用于使用大型基因组规模数据集有效推断进化时间尺度，并将这些新方法集成到我们现有的软件中。我们将开发（1）解释序列错误（以适应DNA降解）并整合过时样本以分析古代基因组的模型，（2）交叉支撑校准以利用基因复制和水平基因转移事件中的信息来推断古代时间尺度，以及（3）有效的MCMC采样算法用于分析物种丰富的基因组数据集。改进后的算法将使我们能够分析来自达尔文生命之树和地球生物基因组项目的数千个基因组的大型数据集，并帮助减少计算分析的碳足迹。最后，我们将使用我们新开发的技术来解决三个重要的案例研究：（1）人类种群和其他原始人的进化，通过在多物种结合模型中整合对古代和现代基因组的分析，（2）推断四足动物物种丰富的生命树（哺乳动物、鸟类、鳄鱼、海龟、鳞龙和两栖动物）在过去三次大灭绝事件中的作用（3）推断真核生物的生命树，通过整合来自古代基因复制和水平基因转移事件的信息，这将有助于减少地球生命历史上一些最深刻的物种形成事件的时间不确定性。