RAPID: Comprehensive Interaction and Annotation (CIA) Analysis of the SARS-CoV-2 Genome and Related Genomes

对导致COVID-19疾病的SARS-CoV-2病原体的RNA序列（即基因组）进行分析，可能有助于更好地诊断、疫苗和治疗该疾病。虽然许多研究小组正在研究SARS-CoV-2基因组，但他们通常侧重于特定的分析方法（例如核苷酸保守分析）或基因组中的一个或另一个元素，因此没有考虑不同的分析如何相互补充，或者SARS-CoV-2基因组元素本身和/或其他因素（例如人类基因和蛋白质或药物）之间的相互作用如何揭示如何治疗该疾病。该项目的重点是采用综合方法识别SARS-CoV-2基因组中的重要元素（这些元素可能被更肤浅的分析所隐藏），并表征SARS-CoV-2基因组内的相互作用以及涉及其他因素（如人类基因、药物和其他病毒）的相互作用。为此，将利用多种互补分析技术（如进化保守分析、多态性功能效应评估、二级RNA结构预测和系统发育信号分析等）来识别和表征SARS-CoV-2基因组的特征。将SARS-CoV-2基因组与其他近亲和远亲病原体和生物体基因组进行比较，以确定可能作为诊断或药物靶点的潜在隐藏或新元素。大规模数据库搜索和计算药物匹配分析不仅无法识别可能受药物调节或影响的SARS-CoV-2基因组区域，而且也无法识别候选药物。全面和综合定量研究的性质将暴露潜在的多学科培训和教育机会。该项目的设计范围将是广泛的，并将尽可能全面地注释SARS-CoV-2基因组，特别是涉及SARS-CoV-2基因组元件之间以及与其他因素（如其他病毒、人类基因、蛋白质和元件（如microrna））以及药物和治疗结构（如反义寡核苷酸（ASO）结构）的基因组引导相互作用。为了进行研究，设计了一个分析管道和工作流程来实现和集成各种分析的结果。该管道首先对所有可用的SARS-CoV-2基因组（目前为10,000个）和近亲和远亲物种（如其他病毒和SARS-CoV-2感染宿主（如蝙蝠和人类））以及功能预测工具进行比较分析，以识别和表征可能的功能元件。鉴定SARS-CoV-2基因组的独特特征可以揭示诊断靶点。对SARS-CoV-2基因组中发现的任何已识别的元件进行结构预测和系统发育信号分析。预测的结构然后受到在硅药物和治疗结构（例如，ASOs）的结合和调制研究。多态性对这些结构可能的功能影响也进行了评估。系统发育信号分析可以揭示功能元件的变异形式可能影响的表型（例如，病毒载量，传染性等）。最后，在网络分析中考虑已确定的元素，以确定它们与哪些其他元素相互作用，以及它们可能影响哪些人类（和其他）宿主基因和蛋白质。该奖项由生物基础设施部生物研究基础设施创新（IIBR信息学）项目利用《冠状病毒援助、救济和经济安全（CARES）法案》提供的资金设立，网站描述和传播作为研究一部分产生的分析结果和数据，网址为https://www.tgen.org.This。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The analysis of the RNA sequence (i.e, a genome) defining the SARS-CoV-2 pathogen, which causes COVID-19 disease, could shed light on better diagnostics, vaccines, and treatments for the disease. Although many research groups are studying the SARS-CoV-2 genome, they typically focus on a particular analytical method (e.g., nucleotide conservation analysis) or one or another element in the genome and therefore do not consider how different analyses may complement one another, or how interactions between the SARS-CoV-2 genomic elements themselves and/or other factors (e.g., human genes and proteins or drugs) could shed light on how to treat the disease. The project focuses on an integrated approach to identifying important elements in the SARS-CoV-2 genome – elements that might be potentially hidden from more superficial analyses – and the characterization of interactions within the SARS-CoV-2 genome as well interactions involving other factors, such as human genes, drugs, and other viruses. To this end, a wide variety of complementary analysis techniques (e.g., evolutionary conservation analysis, polymorphism functional effect evaluation, secondary RNA structure prediction, and phylogenetic signal analysis to name a few) will be exploited to identify and characterize features of the SARS-CoV-2 genome. The SARS-CoV-2 genome to other closely and distantly related pathogen and organism genomes will be compared to identify potentially hidden or novel elements that might be diagnostic or drug targets. Large-scale database searches and computational drug matching analysis will not be able to recognize not only regions of the SARS-CoV-2 genome that might be modulated or impacted by drugs, but also candidate drugs as well. The nature of the comprehensive and integrated quantitative research will expose potential multidisciplinary training and education opportunities.The scope of the project will be broad by design and amount to as comprehensive an annotation of the SARS-CoV-2 genome as possible, especially with respect to genomically-guided interactions involving SARS-CoV-2 genomic elements amongst themselves and with other factors, such as other viruses, human genes, proteins and elements such as microRNAs, and drugs and therapeutic constructs such as antisense oligonucleotide (ASO) constructs. To pursue the research, an analysis pipeline and workflow were designed to enable and integrate the results of various analyses. The pipeline starts with comparative analyses of all available SARS-CoV-2 genomes (currently 10,000) and closely and distantly related species such as other viruses and SARS-CoV-2 infection hosts such as bats and humans (amounting to billions of species) and function prediction tools to identify and characterize likely functional elements. The identification of unique features in the SARS-CoV-2 genome could reveal diagnostic targets. Both structure prediction and phylogenetic signal analyses are pursued on any identified elements found in the SARS-CoV-2 genome. Predicted structures are then subjected to in silico drug and therapeutic construct (e.g., ASOs) binding and modulation studies. The likely functional effects of polymorphism on these structures are also assessed. Phylogenetic signal analyses can reveal phenotypes that variant forms of a functional element can influence (e.g., viral load, infectivity, etc.). Finally, identified elements are considered in network analyses to determine which other elements they interact with and which human (and other) host genes and proteins they could influence. A website describing and disseminating the analysis results and data generated as part of the research will be available at https://www.tgen.org.This RAPID award is made by the Infrastructure Innovation for Biological Research (IIBR Informatics) Program in the Division of Biological Infrastructure, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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.