CAREER: Molecular mechanisms, algorithms and software for design and analysis of genome perturbation experiments

职业：用于设计和分析基因组扰动实验的分子机制、算法和软件

• 批准号: 2238831
• 负责人: Yury Pritykin
• 金额: $ 72.19万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238831&HistoricalAwards=false
• 关键词: CAREER; Molecular; mechanisms; algorithms; software

Genome editing technology using CRISPR (which stands for Clustered Regularly Interspaced Short Palindromic Repeats) has transformed life sciences and is among the most influential inventions in modern biology. CRISPR can be used for guided edits of selected genomic targets. Multiple variants and extensions of CRISPR are actively being used by researchers for editing, knockout, activation and inhibition of genes and genomic elements for studying cell function and identification of important cellular regulators. Computational algorithms and software for experimental design are essential for continued successful use of the CRISPR technologies. However, fundamental molecular mechanisms and optimal parameters of efficient and targeted use of CRISPR technologies across entire genomes are not fully characterized. Limitations in current computational methods for design and analysis of CRISPR experiments include lack of complete unbiased genome-wide information about CRISPR off-targets across biological and experimental contexts, across cell types and across CRISPR variants; lack of specificity to genomes and genomic variants across individuals, cohorts and populations; and incomplete understanding of biases in results of CRISPR experiments caused by off-targets. These limitations present barriers for wider adoption of CRISPR technologies in life sciences and biomedicine. This project is devoted to overcoming these limitations and developing new computational methodology for design and analysis of CRISPR experiments. Fully integrated with this research plan, an educational and outreach program will be developed in order to involve undergraduate and high school students into open collaborative computational biology research. This work will substantially lower the bar for using CRISPR technologies across a wide range of applications and will inform the development of the next generation of such technologies. Implementation of this educational and outreach plan will make entering research careers in bioinformatics and computational biology substantially more accessible for the next generation of students, and will encourage wet lab biologists to obtain computational biology training and to productively collaborate with computational scientists.The goal of this project is to address major challenges in CRISPR low- and high-throughput experimental design and analysis via a combination of computational and experimental approaches. New high-throughput cell type-specific data on genome-wide CRISPR guide RNA on-target and off-target efficiency will be generated and then aggregated into new models of CRISPR efficiency and specificity. New algorithms and software will be developed for generalizable and customizable design, analysis and interpretation of CRISPR genome perturbation experiments across contexts, by incorporating results of the analysis of the newly generated experimental data. Integrated with this research, an educational and outreach program and a specially designed online platform will involve undergraduate and high school students into open collaborative computational biology research, with specific introductory set of research problems about design and analysis of CRISPR experiments. All the results from this project will be available at https://pritykinlab.github.io/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.

使用CRISPR（代表聚集规则间隔短回文重复序列）的基因组编辑技术改变了生命科学，是现代生物学中最具影响力的发明之一。CRISPR可以用于选定的基因组目标的指导编辑。研究人员正积极使用CRISPR的多种变体和扩展，对基因和基因组元件进行编辑、敲除、激活和抑制，以研究细胞功能和鉴定重要的细胞调节因子。计算算法和实验设计软件对于CRISPR技术的持续成功使用至关重要。然而，在整个基因组中有效和有针对性地使用CRISPR技术的基本分子机制和最佳参数尚未完全表征。目前用于设计和分析CRISPR实验的计算方法的局限性包括：缺乏关于CRISPR脱靶的完整、公正的全基因组信息，这些信息涉及生物学和实验背景、细胞类型和CRISPR变体；对个体、群体和人群的基因组和基因组变异缺乏特异性；以及对脱靶导致的CRISPR实验结果偏差的不完全理解。这些限制为CRISPR技术在生命科学和生物医学领域的广泛应用提供了障碍。该项目致力于克服这些限制，并为CRISPR实验的设计和分析开发新的计算方法。与此研究计划完全结合，将开发一个教育和推广计划，以使本科生和高中生参与开放的协作计算生物学研究。这项工作将大大降低在广泛应用中使用CRISPR技术的门槛，并将为下一代此类技术的发展提供信息。这一教育和推广计划的实施将使下一代学生更容易进入生物信息学和计算生物学的研究职业，并将鼓励湿实验室生物学家获得计算生物学培训，并与计算科学家进行富有成效的合作。该项目的目标是通过计算和实验方法的结合来解决CRISPR低通量和高通量实验设计和分析中的主要挑战。将产生新的高通量细胞类型特异性的全基因组CRISPR引导RNA靶上和脱靶效率数据，并将其聚合到CRISPR效率和特异性的新模型中。通过整合新生成的实验数据的分析结果，将开发新的算法和软件，用于跨环境的CRISPR基因组扰动实验的通用和可定制设计、分析和解释。与这项研究相结合，一个教育和推广计划以及一个专门设计的在线平台将让本科生和高中生参与开放的协作计算生物学研究，并提供关于CRISPR实验设计和分析的具体介绍研究问题。该项目的所有结果将在https://pritykinlab.github.io/This上公布，该奖项反映了美国国家科学基金会的法定使命，并通过基金会的智力价值和更广泛的影响审查标准进行评估，认为值得支持。