转录组范围内的遗传干扰的表征
Ajay Nadig1,2,3,4, Joseph M Replogle5,6, Angela N Pogson6
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
bioRxiv : the preprint server for biology
|July 15, 2024
概括
我们开发了TRanscriptome-wide的差异表达分析 (TRADE),以从杂的单细胞CRISPR屏幕数据中揭示微妙的基因表达变化. 贸易揭示了广泛的转录效应,甚至来自微妙的遗传干扰,改善了我们对基因功能的理解.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 系统生物学 系统生物学
背景情况:
- 像Perturb-seq一样,单细胞CRISPR屏幕提供了大规模的遗传干扰的转录基因分析.
- 这些屏幕的噪音数据往往限制了使用传统方法检测真正的生物效应.
研究的目的:
- 介绍TRANScriptome-wide的差异表达分析 (TRADE),这是一个统计框架,用于准确估计噪音基因水平测量的差异表达效应.
- 开发新的指标,如"全转录组影响",以在不同的数据深度中强有力的量化扰动效应.
主要方法:
- 开发了TRADE统计框架,用于分析来自大型Perturb-seq实验的噪音基因表达数据.
- 推导出新的指标来估计真差表达效应的分布和扰动的整体转录影响.
- 应用 TRADE 来分析现有和新的 Perturb-seq 数据集,以及神经精神疾病的病例/对照基因表达数据.
主要成果:
- 证明了许多真正的转录效应被传统分析遗漏,但可以使用TRADE总体检测到.
- 量化了典型的基因扰动影响~45个基因,而基本的基因扰动影响>500个基因在基因组规模的屏幕.
- 鉴定了基因干扰的细胞类型和剂量依赖的转录效应,并发现了转录组相关性,超过神经精神疾病中的遗传相关性.
结论:
- 贸易提供了一个强大的统计基础,用于分析基因屏幕的杂的转录组数据,揭示了广泛的基因调节效应.
- 该框架能够系统地比较遗传扰乱图谱,并增强跨不同生物环境的差异表达分析.
- 贸易有助于更深入地了解基因功能,扰乱效应及其在复杂疾病中的含义.
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