Yang Kevin, Islas Nathaniel, Jewell San, Wu Di, Jha Anupama, Radens Caleb M, Pleiss Jeffrey A, Lynch Kristen W, Barash Yoseph, Choi Peter S
Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Department of Pathology & Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
Nucleic Acids Res. 2025 Jan 24;53(3). doi: 10.1093/nar/gkae1260.
RNA sequencing (RNA-seq) is widely adopted for transcriptome analysis but has inherent biases that hinder the comprehensive detection and quantification of alternative splicing. To address this, we present an efficient targeted RNA-seq method that greatly enriches for splicing-informative junction-spanning reads. Local splicing variation sequencing (LSV-seq) utilizes multiplexed reverse transcription from highly scalable pools of primers anchored near splicing events of interest. Primers are designed using Optimal Prime, a novel machine learning algorithm trained on the performance of thousands of primer sequences. In experimental benchmarks, LSV-seq achieves high on-target capture rates and concordance with RNA-seq, while requiring significantly lower sequencing depth. Leveraging deep learning splicing code predictions, we used LSV-seq to target events with low coverage in GTEx RNA-seq data and newly discover hundreds of tissue-specific splicing events. Our results demonstrate the ability of LSV-seq to quantify splicing of events of interest at high-throughput and with exceptional sensitivity.
RNA测序（RNA-seq）被广泛用于转录组分析，但它存在固有的偏差，阻碍了可变剪接的全面检测和定量。为了解决这个问题，我们提出了一种高效的靶向RNA-seq方法，该方法能极大地富集用于剪接信息的跨连接 reads。局部剪接变异测序（LSV-seq）利用从锚定在感兴趣的剪接事件附近的高度可扩展引物池进行多重逆转录。引物是使用Optimal Prime设计的，这是一种基于数千个引物序列性能训练的新型机器学习算法。在实验基准测试中，LSV-seq实现了高靶向捕获率并与RNA-seq具有一致性，同时所需的测序深度显著降低。利用深度学习剪接代码预测，我们使用LSV-seq靶向GTEx RNA-seq数据中低覆盖度的事件，并新发现了数百个组织特异性剪接事件。我们的结果证明了LSV-seq能够以高通量和极高的灵敏度对感兴趣的事件进行剪接定量。
