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Modeling Heterogenous Outcomes in Functional Genetic Screens Using Single-cell Genomics as Readouts

使用单细胞基因组作为读数对功能遗传筛选中的异质结果进行建模

基本信息

批准号：
10837914
负责人：
Wei Li
金额：
$ 26.88万
依托单位：
CHILDREN'S RESEARCH INSTITUTE
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-08 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10837914
关键词：
Address Adopted Algorithms Area Award Bar Codes Benchmarking Biological Assay Biological Factors Biology CRISPR screen CRISPR/Cas technology Cells Code Collaborations Computer Models Data Data Set Development Elements Funding Future Gene Expression Regulation Genes Genetic Genetic Screening Genetic study Genomics Genotype Measures Memorial Sloan-Kettering Cancer Center Messenger RNA Methods Modeling National Human Genome Research Institute Outcome Paper Parents Pathway interactions Pattern Phenotype Productivity Publications Publishing Regulatory Element Research Source Tail Technology Universities Untranslated RNA Variant Visualization Work algorithm development anticancer research cell type design falls follow-up high throughput technology knockout gene model building novel null mutation response screening single cell analysis single-cell RNA sequencing transcriptome transcriptome sequencing transcriptomics virology

项目摘要

Modeling Heterogenous Outcomes in Functional Genetic Screens Using Single-cell Genomics as Readouts This supplementary request seeks to advance the analysis of single-cell CRISPR screening datasets by developing novel computational models to address the challenge of heterogeneous transcriptome responses upon perturbing the same gene. It falls within the scope of the parent award to develop algorithms for functional genetic screens, but proposes to follow up on unanticipated results (single-cell CRISPR screen) and enhance this direction, which is unexpectedly productive, as is demonstrated in preliminary results. Specifically, we aim to: (1) build a model to detect heterogeneous expression patterns in Perturb-seq assays, l, and (2) extend the model to other dataset types, including pooled single-cell and bulk transcriptomics datasets from various gene perturbations. This work will significantly enhance our understanding of genotype-phenotype relationships at the single-cell level, with broad applications in cancer research, virology, and gene regulation.

使用单细胞基因组学作为功能遗传筛选的异质性结果建模读数该补充请求旨在通过以下方式推进单细胞CRISPR筛选数据集的分析：开发新的计算模型，以应对异质性转录组反应的挑战干扰了同一个基因它属于福尔斯的范围内的母公司奖开发算法，功能性基因筛选，但建议跟进意外结果（单细胞CRISPR筛选），加强这一方向，这是出乎意料的生产力，如初步结果所示。具体地，我们的目标是：（1）建立一个模型来检测Perturb-seq测定中的异质性表达模式，以及（2）将该模型扩展到其他数据集类型，包括合并的单细胞和批量转录组学数据集各种基因干扰的结果这项工作将显著增强我们对基因型-表型的理解在单细胞水平的关系，在癌症研究，病毒学和基因调控的广泛应用。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches.

使用机器学习方法对 CRISPR-Cas13d 的靶向和脱靶效应进行建模

DOI：
10.1038/s41467-023-36316-3
发表时间：
2023-02-10
期刊：
NATURE COMMUNICATIONS
影响因子：
16.6
作者：
Cheng, Xiaolong;Li, Zexu;Shan, Ruocheng;Li, Zihan;Wang, Shengnan;Zhao, Wenchang;Zhang, Han;Chao, Lumen;Peng, Jian;Fei, Teng;Li, Wei
通讯作者：
Li, Wei