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流逝而积累的变化，记录了物种差异和灭绝的进化史，就像钟表一样。通过对基因组进行测序并整合化石记录的信息，科学家可以重建进化树并将其校准到地质时代。这种分子锁定的方法通过利用测序基因组的信息而导致了壮观的结果，极大地丰富了我们对漫画多样化物种多样化过程的宏观进化过程的理解，以及可能的驱动因素以及主要的地质事件和古气候变化等可能的驱动因素，例如，bayesisian推理为整合了分析的各种范围的范围，这些框架是分析的各种范围，这些框架是分析的各种范围，这些框架是分析的各种范围，这些框架是分析的，分析了分析的分析。时间，化石对物种系统发育的年龄和放置，以及基因组数据的估计分子分支长度。但是，贝叶斯的方法在计算上很昂贵，因为它们需要随机的MCMC采样以整合不确定性，因此他们无法处理基因组数据的泛滥。 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学术受益人用于重建此类时间表。该提案的总体目的是开发新的模型和算法，以使用大型基因组尺度数据集有效地推断进化时尺度，并将这些新方法集成到我们现有的软件中。 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 horizo​​ntal gene transfer events for inferring ancient timescales, and (3) efficient MCMC sampling algorithms for analysis of species-rich phylogenomic datasets.改进的算法将使从达尔文的生命和地球生物基因组项目中分析数千种基因组的大型数据集，并有助于减少计算分析的碳足迹，从本文中，我们将使用新近开发的技术来解决三个重要的案例研究：（1）在人类群体和其他人群的进化中，通过构成人类的进化，并通过建立了其他分析，并通过建立了众多的人口，并通过居住的人群进行了整合。具有渗入的结合模型，（2）在三场过去的三个主要灭绝事件（End-Permian，permian，Triassic，Triassic-jurassic，and endere and the the the Gloll and and the extere and the extrace and the exte and the ey and the e e anderal and the e e eferece and of the e eferece and the ETERE）中，推断四足（哺乳动物，鸟类，鸟类，鳄鱼，海龟，鳞翅目和两栖动物）的富含物种的生命之树。通过整合古代基因复制和水平基因转移事件的信息来推断真核生物的生命树，这将有助于减少地球生命历史上一些最深层的物种事件的不确定性。