Computational methods for pandemic-scale genomic epidemiology

大流行规模基因组流行病学的计算方法

• 批准号: MR/Z503526/1
• 负责人: Nick Goldman
• 金额: $ 61.37万
• 依托单位: European Bioinformatics Institute
• 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=MR%2FZ503526%2F1
• 关键词: Computational; methods; pandemic; scale; genomic

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.

来自感染性病原体的基因组序列的系统发育分析可以揭示有关其进化和传播病史的基本信息。正如共同19的大流行所体现的那样，这些分析和数据在流行病学中起着至关重要的作用，对于跟踪和重建传染病在社区内和国家之间的传播至关重要。了解传播的动力；估计遏制措施的功效；预测流行病学动力学；为了监测病原体的进化，如鉴定新的SARS-COV-2突变和关注的变体所表明的。随着基因组测序技术的持续改进和广泛采用，基因组流行病学将成为关键的医疗资产。因此，对基因组流行病学数据分析方法的改进不仅将帮助我们解决正在进行的传染病流行病，而且还将增强我们对未来大流行病的准备。但是，目前对基因组流行病学数据的研究主要是基于计算方法，而不是根据其需求量身定制的，而是针对他们的需求量身定制的。因此，对大型基因组序列数据集的最理想的分析，例如当前在19号大流行期间出现的数据集，目前是不可行的。在该项目中，我们通过开发针对大流行基因组流行病学量身定制的计算方法来解决这一限制。这些方法将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的几个优先事项，例如“全球健康”，“感染和免疫”，“抗菌耐药性”和“生物医学和健康数据科学”。我们的具体目的是：1）开发基因组流行病学的算法。我们将开发用于分析基因组序列数据的新算法。我们将利用这样一个事实，即基因组流行病学中的序列通常非常紧密，因此彼此非常相似，以设计为该领域量身定制的算法和数学方法。根据我们过去的经验，我们期望这些方法比传统方法高得多：允许对数百万而不是数千个基因组序列进行分析。2）提高现实主义和准确性。高度可变的突变速率和复发序列误差，而常见的是当前基因组流行病学分析引起误差和不确定性。为了提高我们的方法的准确性而不会影响其效率，我们将开发基因组进化的定制数学模型，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了道路。我们将开发一个有效的开源软件库，以轻松将我们的新方法集成到其他高度影响力的软件包中，以分析遗传数据。这将允许我们的方法的最广泛应用，因为用户将能够采用它们进行各种分析，例如对传输历史的估计或及时识别所关注的变体。4）启用大流行级的贝叶斯系统。贝叶斯系统发育学是基因组流行病学中最先进的应用的核心，例如植物地理学（对边界内和边界内病原体的扩散的研究）和系统动力学（病原体流行的研究随时间变化）。我们将将我们的方法整合到广泛使用的贝叶斯系统发育包装野兽中，以允许分析数百万基因组的数据集。