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（由申请人提供）：CRISPR/Cas9 系统是一种革命性的哺乳动物细胞基因组编辑方法，CRISPR/Cas9 敲除技术以及与效应蛋白融合的 dCas9 的最新发展使得高通量、经济高效的基因功能筛选成为可能。我们开发了一种基于模型的全基因组 CRISPR/Cas9 敲除分析 (MAGeCK) 方法，用于从基因组规模的 CRISPR/Cas9 中调用基因和通路。 MAGeCK 表现出比以前的方法更好的性能，并从几个已发布的屏幕中识别出稳健的命中，包括新颖的命中。在本提案中，我们的目标是开发统计和计算方法来改进 MAGeCK 算法，以实现质量控制、数据分析和分析。在一个统一的统计模型中，该方法在 gRNA 水平上校正批次效应，同时估计 gRNA 效率和基因选择，并识别多种条件下的差异基因和通路选择，并考虑测序偏差和细胞倍增时间。 。具体来说，我们建议： 目标 1. 开发用于 CRISPR 筛选的可靠数据标准化方法 目标 2. 开发统计和计算框架，以从多个 CRISPR 筛选实验和条件中调用细胞和条件特异性的必需基因和通路 目标 3. 开发方法。减轻离群 gRNA 效应并使用蛋白质相互作用网络增强 CRISPR 筛选基因调用的性能。目标 4. 开发用户友好的软件功能，例如用于 CRISPR 筛选的质量控制、可视化、设计和分析软件。在这些研究结束时，我们将在不同的实验环境下开发出更通用、更可靠的 CRISPR 筛选分析算法，这些方法可应用于 CRISPR 敲除筛选、CRISPRi/a 筛选、基于测序的 si/shRNA 筛选和 CRISPR 筛选。表型可以是细胞生长、迁移、分化或 GFP 标记基因表达的分选，我们提出的方法将极大地促进许多实验生物学小组采用该技术，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 筛选来进行研究。回答重要的生物学问题关于基因调控和药物反应。