Computational Methods for Genome-Wide CRISPR Screens

全基因组 CRISPR 筛选的计算方法

• 批准号: 9350386
• 负责人: Xiaole Shirley Liu
• 金额: $ 51.91万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-09 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9350386
• 关键词: Adopted; Adoption; Algorithmic Analysis; Algorithms; Behavior; Bioinformatics; Biological; Biological Markers; Biological Process; Biology; CRISPR interference; CRISPR screen; Cell Line; Cells; Clustered Regularly Interspaced Short Palindromic Repeats; Communities; Computer Analysis; Computer Simulation; Computer software; Computing Methodologies; Custom; DNA Sequence; Data; Data Analyses; Development; Disease; Drug resistance; Essential Genes; Evaluation; Experimental Designs; Gene Expression; Gene Expression Regulation; Generations; Genes; Genomics; Goals; Guide RNA; Imagery; Knock-out; Label; Libraries; Mammalian Cell; Methodology; Methods; Modeling; Pathway interactions; Performance; Pharmaceutical Preparations; Phenotype; Physiological Processes; Proteins; Publishing; Quality Control; Regulator Genes; Research; Somatic Cell; Sorting - Cell Movement; Statistical Methods; Statistical Models; System; Techniques; Technology; Time; Validation; Work; base; cell growth; cell motility; computer framework; cost effective; data visualization; design; experimental study; genetic analysis; genome editing; genome-wide; genome-wide analysis; improved; interest; migration; novel; prototype; public health relevance; response; screening; small hairpin RNA; stem cell differentiation; tumorigenesis; user friendly software; user-friendly

 DESCRIPTION (provided by applicant): The CRISPR/Cas9 system is a revolutionary approach for genome editing of mammalian cells. Recent developments in CRISPR/Cas9 knockout technology as well as dCas9 fused with effector proteins enable high throughput cost-effective gene functional screens but create computational challenges. We have developed a Model-based Analysis of Genome-wide CRISPR/Cas9 Knockout (MAGeCK) method for calling genes and pathways from genome-scale CRISPR/Cas9 screens. MAGeCK demonstrates better performance than previous methods and identify robust hits, including novel ones, from several published screens. In this proposal, we aim to develop the statistical and computational methods to improve the MAGeCK algorithm to enable quality control, data analysis, and interactive visualizations of CRISPR screen data. In one unified statistical model, the proposed method corrects batch effect at gRNA level, simultaneously estimates gRNA efficiency and gene selection, and identifies differential gene and pathway selection over multiple conditions, and considers sequencing bias and cell doubling time. Specifically, we propose to: Aim 1. Develop robust data normalization methods for CRISPR screens. Aim 2. Develop the statistical and computational framework to call cell- and condition-specific essential genes and pathways from multiple CRISPR screen experiments and conditions. Aim 3. Develop methods to mitigate outlier gRNA effects and use protein interaction network to enhance the performance of CRISPR screen gene calling. Aim 4. Develop user-friendly software features, such as quality control, visualization, design and analysis software for CRISPR screens. At the conclusion of these studies, we will have developed more versatile and reliable analysis algorithms for CRISPR screens under diverse experimental settings. These methods could be applied to CRISPR knockout screens, CRISPRi/a screens, sequencing-based si/shRNA screens, and the phenotype could be cell growth, migration, differentiation, or sorting of GFP-labeled gene expression. Our proposed methods will greatly facilitate the technology adoption to many experimental biology groups, so they can use the powerful genome-wide CRISPR screens under diverse experimental settings to answer important biological questions about gene regulation and drug response.

 描述(申请人提供)：CRISPR/CAS9系统是哺乳动物细胞基因组编辑的革命性方法。CRISPR/Cas9基因敲除技术以及与效应蛋白融合的dCas9技术的最新发展使高通量、高成本效益的基因功能筛选成为可能，但也带来了计算方面的挑战。我们开发了一种基于模型的全基因组CRISPR/Cas9基因敲除分析(MAGeCK)方法，用于从基因组规模的CRISPR/Cas9筛选中调用基因和途径。MAGeCK显示出比以前的方法更好的性能，并从几个已发布的屏幕上识别出包括新的在内的强大的命中。在这项提案中，我们的目标是开发统计和计算方法来改进MAGeCK算法，以实现CRISPR屏幕数据的质量控制、数据分析和交互可视化。在一个统一的统计模型中，该方法在gRNA水平校正批次效应，同时估计gRNA效率和基因选择，识别多种条件下的差异基因和途径选择，并考虑测序偏差和细胞倍增时间。具体地说，我们建议：目标1.开发稳健的CRISPR屏幕数据归一化方法。目的2.开发统计和计算框架，从多个CRISPR筛选实验和条件中调用特定于细胞和条件的必要基因和途径。目的3.开发减轻异常值gRNA效应的方法，并利用蛋白质相互作用网络来提高CRISPR筛选基因调用的性能。目的4.开发用户友好的软件功能，如CRISPR屏幕的质量控制、可视化、设计和分析软件。在这些研究的结论下，我们将开发出更通用和可靠的CRISPR屏幕在不同的实验设置下的分析算法。这些方法可应用于CRISPR基因敲除筛选、CRISPRi/a筛选、基于测序的si/shRNA筛选，其表型可以是细胞生长、迁移、分化或GFP标记基因表达的分类。我们提出的方法将极大地促进许多实验生物学小组采用技术，因此他们可以在不同的实验环境下使用强大的全基因组CRISPR屏幕来回答关于基因调控和药物反应的重要生物学问题。