Computational Methods for Mapping Genetic Interactions in Human Cells

项目摘要 尽管我们有效测量人类基因组的能力不断提高，但我们仍然面临重大挑战， 解读它们。即使对于疾病或其他具有强烈遗传成分的特征，我们通常仍然 无法从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， 仍然不了解基因组中不同位点的变异如何结合联合收割机来影响生物体的规则。 基因并不是独立作用的，一个基因变化的效果在很大程度上取决于是否存在 基因组中的其他变体。 在过去的二十年里，在模式生物如酵母中进行了广泛的工作， 探索基因如何相互作用导致表型的基本原理。特别是， 在全球范围内系统地引入了精确工程突变的组合， 测量它们对细胞的影响。这项工作表明，系统的组合基因组 微扰可以是一种强有力的策略，以了解基因组是如何在功能上组织的， 精确地阐明特定组分的功能作用。虽然技术挑战以前 限制了高等生物的类似努力，新的破坏性CRISPR/Cas9基因组编辑技术 现在使得这种强大的组合突变方法在人类基因组上成为可能。不过虽然 现在的实验技术可以在全基因组范围内系统地操纵人类基因组， 虽然我们的研究规模很大，但我们仍然缺乏解释所得数据所需的计算方法。 这项建议的具体目标是开发新的计算方法，直接支持 基于CRISPR/Cas9的人类细胞遗传相互作用的系统作图和分析 技术.我们将通过三个具体目标来实现我们的目标：（1）开发计算 用于测量人类全基因组CRISPR/Cas9筛选的定量遗传相互作用的模型 细胞，（2）开发算法的最佳查询选择，使有效的战略，映射人类 遗传交互作用，以及（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。 拟议的研究是创新的，因为它连接了十多年来建立的概念， 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，将广泛适用于大规模CRISPR/Cas9- 基于筛选工作。

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.