Computational Methods for Mapping Genetic Interactions in Human Cells

绘制人类细胞遗传相互作用的计算方法

• 批准号: 10241348
• 负责人: Chad L Myers
• 金额: $ 36.84万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-25 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10241348
• 关键词: Address; Affect; Algorithms; Animal Model; Area; Biology; CRISPR screen; CRISPR/Cas technology; Cell Line; Cells; Chemicals; Chromosome Mapping; Computer Analysis; Computer Models; Computing Methodologies; Custom; Data; Data Analyses; Development; Disease; Engineering; Face; Future; Genes; Genetic; Genetic Screening; Genome; Genomics; Guide RNA; Human; Human Biology; Human Gene Mapping; Human Genome; Libraries; Light; Maps; Measurement; Measures; Methods; Modeling; Mutation; Organism; Outcome; Pharmaceutical Preparations; Phenotype; Quantitative Genetics; Research; Resources; Role; Sampling; System; Technology; Time; Tissues; Variant; Work; Yeast Model System; Yeasts; base; biological systems; combinatorial; computer infrastructure; computerized tools; design; effectiveness testing; exhaustion; fitness; functional genomics; gene function; genetic analysis; genetic variant; genome editing; genome wide screen; genome-wide; genomic data; human genome sequencing; innovation; insight; mutant; novel; screening; trait

Project Summary Despite our increasing capabilities to efficiently measure human genomes, we still face major challenges in interpreting them. Even for diseases or other traits that have a strong genetic component, we are generally still unable to accurately predict disease status from genome sequence. One major reason for this gap is that we still do not understand the rules for how variants at different loci in the genome combine to affect an organism. Genes do not act independently, and the effect of one genetic change can depend greatly on the presence of other variants in a genome. Over the past two decades, extensive work has been carried out in model organisms such as yeast to explore the basic principles that govern how genes interact to cause phenotypes. In particular, several efforts have systematically introduced combinations of precisely engineered mutations on a global scale and measured their effects on cells. This work has demonstrated that systematic combinatorial genome perturbation can be a powerful strategy to understand how a genome is functionally organized and can precisely elucidate the functional role of the specific components. While technical challenges have previously limited similar endeavors in higher organisms, new disruptive CRISPR/Cas9-based genome editing technology now makes this powerful combinatorial mutation approach possible on the human genome. However, although the experimental technology now exists to systematically manipulate the human genome on a genome-wide scale, we still lack the computational approaches necessary for interpreting the resulting data. The specific objective of this proposal is to develop new computational methods that directly support the systematic mapping and analysis of genetic interactions in human cells based on CRISPR/Cas9 technology. We will accomplish our objective by focusing on three specific aims: (1) develop computational models for measuring quantitative genetic interactions from genome-wide CRISPR/Cas9 screens in human cells, (2) develop algorithms for optimal query selection that enable efficient strategies for mapping human genetic interactions, and (3) apply optimal screen selection algorithm to develop a scalable screening platform for functional profiling of chemical perturbations and genetic variants. The proposed research is innovative in that it bridges concepts established over a decade of work on genetic interactions in yeast with the latest developments in genome editing technology. Our proposed work will establish new, robust computational tools that will be broadly applicable to large-scale CRISPR/Cas9- based screening efforts.

项目摘要 尽管我们提高了有效测量人类基因组的能力，但我们在 解读它们。即使对于疾病或其他具有很强遗传成分的特征，我们通常也是 无法根据基因组序列准确预测疾病状态。造成这种差距的一个主要原因是，我们 仍然不了解基因组中不同位置的变异如何组合在一起影响有机体的规则。 基因并不是独立起作用的，一个基因变化的效果在很大程度上取决于 基因组中的其他变种。 在过去的二十年里，人们在酵母等模式生物中开展了广泛的工作，以 探索控制基因如何相互作用以产生表型的基本原则。特别是，有几项努力 在全球范围内系统地引入了精确工程突变的组合，并 测量它们对细胞的影响。这项工作证明了系统的组合基因组 微扰可以是一种强大的策略来理解基因组是如何功能组织的，并且可以 准确阐明特定成分的功能作用。虽然之前的技术挑战 在高等生物中有限的类似努力，基于CRISPR/Cas9的新的颠覆性基因组编辑技术 现在使这种强大的组合突变方法在人类基因组上成为可能。然而，尽管 这项实验技术现在已经存在，可以在全基因组范围内系统地操纵人类基因组 尽管如此，我们仍然缺乏必要的计算方法来解释结果数据。 这项提议的具体目标是开发新的计算方法，直接支持 基于CRISPR/CAS9的人类细胞遗传互作系统定位与分析 技术我们将通过专注于三个具体目标来实现我们的目标：(1)开发计算 从全基因组CRISPR/Cas9屏幕测量人类定量遗传交互作用的模型 细胞，(2)开发最优查询选择的算法，使得能够有效地映射人类 遗传交互作用；(3)应用最优筛选算法开发可扩展的筛选平台 用于化学扰动和遗传变异的功能分析。 拟议的研究具有创新性，因为它将十年来在以下方面建立起来的概念联系起来 酵母中的遗传相互作用与基因组编辑技术的最新发展。我们建议的工作 将建立新的、强大的计算工具，这些工具将广泛适用于大规模CRISPR/CAS9- 基于筛查的努力。