Computational methods for pandemic-scale genomic epidemiology

传染性病原体基因组序列的系统发育分析可以揭示其进化和传播历史的基本信息。正如2019冠状病毒病大流行所示，这些分析和数据在流行病学中发挥着至关重要的作用，对于追踪和重建社区内和国家之间的传染病传播至关重要；了解传播的动态；评估控制措施的效果；预测流行病学动态；并监测病原体的进化，如发现新的SARS-CoV-2突变和令人关注的变体。随着基因组测序技术的不断改进和广泛采用，基因组流行病学将成为一项关键的医疗资产。因此，基因组流行病学数据分析方法的改进不仅将帮助我们应对当前的传染病流行，而且还将加强我们对未来流行病的准备。然而，目前对基因组流行病学数据的调查主要基于计算方法，这些方法不是根据他们的需要量身定制的，而是为进化生物学研究开发的，而进化生物学研究通常只考虑少数高度分化的基因组。因此，对大型基因组序列数据集（例如在COVID-19大流行期间出现的数据集）进行的最理想的分析目前是不可行的。在这个项目中，我们通过开发适合大流行规模的基因组流行病学的计算方法来解决这一限制。这些方法将能够对大型基因组流行病学数据集进行准确的实时分析。这些目标属于MRC的若干优先事项，如“全球健康”、“感染和免疫”、“抗菌素耐药性”和“生物医学和健康数据科学”。我们的具体目标是：1)开发基因组流行病学的算法。我们将开发新的算法来分析基因组序列数据。我们将利用基因组流行病学中的序列通常非常密切相关，因此彼此非常相似的事实，设计适合该领域的算法和数学方法。根据我们过去的经验，我们期望这些方法比传统方法效率高数千倍：允许分析数百万而不是数千个基因组序列。2)增加真实感和准确性。高度可变的突变率和反复出现的序列错误虽然很常见，但在当前的基因组流行病学分析中造成了错误和不确定性。为了提高我们方法的准确性而不影响其效率，我们将开发定制的基因组进化数学模型，考虑到基因组流行病学数据的这些复杂性。3)为更广泛的实施铺平道路。我们将开发一个高效的开源软件库，以便轻松地将我们的新方法集成到其他具有高度影响力的软件包中，用于分析遗传数据。这将使我们的方法得到最广泛的应用，因为用户将能够采用它们进行各种分析，例如对传播历史的估计或对关注的变体的及时识别。4)启用大流行规模的贝叶斯系统发育。贝叶斯系统遗传学是基因组流行病学中最先进应用的核心，例如系统地理学（研究病原体在边界内和边界之间的传播）和系统动力学（研究病原体流行率随时间的变化）。我们将在广泛使用的贝叶斯系统发育包BEAST中整合我们的方法，以允许对数百万个基因组的数据集进行分析。

Phylogenetic analyses of genome sequences from infectious pathogens can reveal essential information regarding their evolution and transmission history. As the COVID-19 pandemic exemplified, these analyses and data play a crucial role in epidemiology and are essential to track and reconstruct the spread of infectious disease within communities and between countries; to understand the dynamics of transmission; to estimate the efficacy of containment measures; to predict epidemiological dynamics; and to monitor pathogen evolution as showcased by the identification of new SARS-CoV-2 mutations and variants of concern.With ongoing improvement and widespread adoption of genome sequencing technologies, genomic epidemiology will become a key medical asset. Improvements to genomic epidemiological data analysis methods therefore will not only help us tackle ongoing infectious disease epidemics, but will also enhance our preparedness towards future pandemics.However, current investigations of genomic epidemiological data are predominantly based on computational methods that are not tailored to their needs, but rather were developed for evolutionary biology studies where typically few, highly diverged genomes are considered. Most desirable analyses of large genome sequence data sets, such as those that emerged during the COVID-19 pandemic, are thus currently unfeasible.In this project we address this limitation by developing computational methods tailored for pandemic-scale genomic epidemiology. These methods will enable accurate real-time analyses of large genomic epidemiological data sets. These objectives fall within several priorities of the MRC, such as "Global health", "Infections and immunity", "Antimicrobial resistance", and "Biomedical and health data science". Our specific aims are to:1) Develop algorithms for genomic epidemiology. We will develop new algorithms for analysing genome sequence data. We will exploit the fact that sequences in genomic epidemiology are typically very closely related, and thus very similar to each other, to devise algorithms and mathematical approaches tailored for this field. Based on our past experience, we expect these approaches to be thousands of times more efficient than traditional methods: allowing the analysis of millions rather than thousands of genome sequences.2) Increase realism and accuracy. Highly variable mutation rates and recurrent sequence errors, while common, cause errors and uncertainty in current genomic epidemiological analyses. To increase the accuracy of our methods without affecting their efficiency, we will develop bespoke mathematical models of genome evolution that take into account these complexities of genomic epidemiological data.3) Pave the way to wider implementation. We will develop an efficient open-source software library to easily integrate our new methods within other highly impactful software packages for the analysis of genetic data. This will allow the broadest application of our methods, as users will be able to adopt them for a variety of analyses, such the estimation of transmission histories or the timely identification of variants of concern.4) Enable pandemic-scale Bayesian phylogenetics. Bayesian phylogenetics is at the core of most advanced applications in genomic epidemiology, such as phylogeography (the study of the spread of pathogens within and between borders) and phylodynamics (the study of pathogen prevalence changes through time). We will integrate our methods within the widely used Bayesian phylogenetic package BEAST to allow the analysis of data sets of millions of genomes.