EAGER: Understanding the genomes and strain mutations of SARS-CoV-2 and other viruses using comparative and population genetic approaches

比较基因组学提供了一个强大的透镜，其中了解新的基因组。EAGER奖旨在通过开发和应用创新的比较基因组和群体遗传学方法，利用这些领域研究人员的专业知识，对SARS-CoV-2和其他病毒的基因组和菌株突变有更深入的科学了解。这些分析将阐明进化力量的相对贡献，这些进化力量塑造了SARS-CoV-2基因组和其他病毒基因组，跨越了深度和最近的进化时间尺度，并将确定其基因组中功能重要的位置。它们将为更广泛的科学界的努力提供信息，以模拟感染的动态，缓解策略和开发治疗方法。这项研究将被整合到教育项目中，利用BIG夏季本科生研究计划和加州大学洛杉矶分校的计算基因组学夏季研究所，具体目标是利用各种冠状病毒基因组的多基因组比对，开发和应用SARS-CoV-2的比较基因组分析。这项工作将利用最近开发的ConsHMM方法，以单核苷酸分辨率将SARS-CoV-2基因组注释为不同的保守状态。ConsHMM定义的保守状态的基础上的组合和空间模式的物种对齐和匹配的参考基因组使用多变量隐马尔可夫模型（HMM）。这些保守状态将与SARS-CoV-2基因组的其他注释相关，包括（间）基因和关键蛋白区域。群体遗传学方法将应用于SARS-Cov-2基因组的公开序列，以推断突变的基本特性。为了估计这些参数，将开发新的基于结构化聚结的模型，以解释多个时间点和地点的人口统计过程。虽然最初的调查将集中在大量收集的SARS-CoV-2基因组，但开发的方法将适用于病毒基因组的研究（如其他冠状病毒、流感、埃博拉、艾滋病毒）该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.

Comparative genomics provides a powerful lens in which to understand novel genomes. This EAGER award sees to gain a deeper scientific understanding of the genomes and strain mutations of SARS-CoV-2 and other viruses by developing and applying innovative comparative genomic and population genetic methods leveraging the expertise of the investigators in these areas. These analyses will elucidate the relative contributions of evolutionary forces that shape the SARS-CoV-2 genome and other virus genomes across deep and recent evolutionary time scales and will pinpoint functionally important positions in their genomes. They will inform efforts of the broader scientific community to model the dynamics of infection, strategies for mitigation, and to develop therapeutics. This research will be integrated into educational programs that leverage the BIG summer undergraduate research program and the Computational Genomics Summer Institute at UCLA.The specific aims are to develop and apply comparative genomic analyses for SARS-CoV-2 using multiple genome alignments of various coronavirus genomes. The work will leverage the recently developed ConsHMM method to annotate the SARS-CoV-2 genome at single nucleotide resolution into different conservation states. ConsHMM defines conservation states based on the combinatorial and spatial patterns of which species align to and match a reference genome using a multivariate hidden Markov model (HMM). These conservation states will be related to other annotations of the SARS-CoV-2 genome including (inter)genic and key protein regions. Population genetic approaches will be applied to publicly available sequences of the SARS-Cov-2 genome to infer fundamental properties of mutations. To estimate these parameters, novel structured coalescent-based models will be developed that account for demographic processes across multiple time points and locations. While the initial investigation will focus on the large collections of SARS-CoV-2 genomes that are becoming available, the methods developed will be applicable to the study of viral genomes (such as other coronaviruses, influenza, ebola, and HIV) and their strain mutations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.