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Updated: Jun 23, 2025

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

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Published on: June 24, 2021

替代多化调节剂的多复合单细胞特征

Madeline H Kowalski¹, Hans-Hermann Wessels², Johannes Linder³

¹New York Genome Center, New York, NY, USA; Center for Genomics and Systems Biology, New York University, New York, NY, USA; New York University Grossman School of Medicine, New York, NY, USA.

|June 26, 2024

概括

这项研究引入了CPA-Perturb-seq以映射裂变和多化 (CPA) 蛋白如何控制基因表达多样性. 这些发现揭示了RNA处理因子对多A位点的协调调节,进步了我们对转录后调节的理解.

科学领域:

分子生物学
基因组学
计算生物学

背景情况:

哺乳动物的基因利用多个多化 (polyA) 位点,产生显著的转录多样性.
分裂和多化 (CPA) 机器精确地调节了PolyA位点的选择.
了解控制多A位点选择的机制对于解读基因表达调节至关重要.

研究的目的:

研究CPA蛋白如何影响多A位点使用和转录多样性.
开发和应用一种用于分析多A位点调节的新选方法.
确定规范模块和规范规范代码来规范PolyA地点的选择.

主要方法:

CPA-Perturb-seq:使用3'单细胞RNA测序 (scRNA-seq) 的多重扰动屏幕.
开发一个计算框架来检测扰动诱导的多化变化.
训练和验证深度神经网络 (APARENT-Perturb) 以预测双重多A站点的使用情况.

主要成果:

与不同核RNA处理事件 (延长,拼接,终止,监控) 相关的共同调节的PolyA位点模块的识别.
对CPA扰动的反应进行预测的cis-regulatory代码的表征.
影响多A位点选择的不同调节复合体之间的相互作用的发现.

结论:

多复合单细胞扰动屏幕是研究后转录调节的强大工具.
CPA调节器通过多种RNA处理途径协调多A位点的使用.
开发的深度学习模型准确地预测了多A站点的使用和监管相互作用.

关键词:

波动后的时间进行RNA处理替代性多化分裂和多化转录后调节

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