Computational Methods for Designing Optimal Genomics-guided Viral Diagnostics

项目摘要/摘要 病毒基因组测序呈指数级增长，尖端分子技术在 基因组数据在检测和应对病毒方面显示出巨大的希望。然而，我们缺乏计算能力 框架，有效地利用病毒数据来设计由这些应用的核酸或氨基酸序列 技术。该提案提供了一个职业发展计划，以(I)建立计算技术- 算法、模型和软件-提供高精度的诊断分析，具有超越市场的潜力 现有的技术，以及(Ii)使用这些技术来主动设计检测1,000种病毒的检测方法。 该项目将首先开发设计最佳病毒基因组信息诊断的方法。这项研究将 制定目标函数，以评估预期病毒分布的化验性能 目标。在研究中构建的组合优化算法和生成模型将进行优化 这些功能。该项目还将开发数据集，用于训练分析性能的预测模型， 用于目标函数，侧重于CRISPR诊断、扩增诊断和基于抗原的诊断。 初步实验结果表明，这样的模型可以呈现出精致的灵敏度和 专一性。这项研究将把算法设计的化验与四种病毒的最先进测试进行比较。 利用这些方法，该项目将设计具有物种特异性和广泛有效的诊断分析方法。 所有已知感染脊椎动物的病毒的基因组多样性。这项研究将建立一个系统来监测 检测针对新出现的病毒基因组多样性的有效性，并根据需要不断更新它们。至 如果能够广泛采用这些方法，该项目将在可访问的软件中有效地实施这些方法。 该提案与NIAID通过数据科学改进诊断的目标一致。这里开发的方法 可能还有助于治疗和疫苗设计，并将使世界更好地准备抗击病毒爆发。 职业发展奖将为应聘者提供长期感兴趣的应用领域的培训 他的事业。应聘者以前在开发计算方法和分析病毒方面有经验 基因组。通过该奖项，他将获得诊断应用方面的新知识和技能，以及 免疫学、生物工程和相关实验室技术方面的正式和非正式培训。这次培训将 帮助候选人在治疗和疫苗应用方面取得进展，这些治疗和疫苗应用可能受益于先进的 计算方法。博德学院为候选人提供了一个支持性的环境 发展，包括职业发展讲习班、与拟议计划相一致的研究研讨会、 以及与拥有与候选人互补的专业知识的科学家开展合作的机会。 研究和培训将帮助他形成一个独立的研究小组，专注于开发和 应用计算方法，以实现更有效的微生物监测和反应。

Project Summary/Abstract Viral genome sequencing is growing exponentially and cutting-edge molecular technologies, guided by genomic data, show great promise in detecting and responding to viruses. Yet we lack a computational framework that efficiently leverages viral data to design the nucleic or amino acid sequences applied by these technologies. The proposal provides a career development plan to (i) build computational techniques — algorithms, models, and software — that yield highly accurate diagnostic assays, with potential to outperform existing ones, and (ii) use the techniques to proactively design assays for detecting 1,000s of viruses. The project will first develop methods for designing optimal viral genome-informed diagnostics. The study will formulate objective functions that evaluate an assay’s performance across a distribution of anticipated viral targets. Combinatorial optimization algorithms and generative models, constructed in the study, will optimize the functions. The project will also develop datasets for training predictive models of assay performance, which are used in the objective functions, focusing on CRISPR-, amplification-, and antigen-based diagnostics. Preliminary experimental results suggest such models can render assays with exquisite sensitivity and specificity. The study will compare the algorithmically-designed assays to state-of-the-art tests for four viruses. With these methods, the project will design diagnostic assays that are species-specific and broadly effective across genomic diversity for all viruses known to infect vertebrates. The study will build a system to monitor the assays’ effectiveness against emerging viral genomic diversity and to continually update them as needed. To enable the broad adoption of these methods, the project will implement them efficiently in accessible software. The proposal aligns with a NIAID goal of improving diagnostics via data science. The methods developed here may also aid therapy and vaccine design, and will leave the world better prepared to combat viral outbreaks. The career development award will provide training for the candidate in applied areas of long-term interest to his career. The candidate has previous experience in developing computational methods and analyzing viral genomes. Through the award, he will gain new knowledge and skills in diagnostic applications, alongside formal and informal training in immunology, bioengineering, and related laboratory techniques. This training will help the candidate progress toward therapy and vaccine applications that could benefit from advanced computational methods. The Broad Institute provides a supportive environment for the candidate’s development, including career development workshops, research seminars aligned with the proposed plan, and opportunities to initiate collaborations with scientists having expertise complementary to the candidate’s. The research and training will help him form an independent research group focused on developing and applying computational methods to enable more effective microbial surveillance and response.