Machine learning-optimized targeted detection of alternative splicing
RNA-sequencing (RNA-seq) is widely used for analysis of alternative splicing, but in practice, has inherent biases which hinder its ability to detect and quantify splicing events. To address this, we present a targeted RNA-seq method that specifically 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 splice junctions of interest. Primers are designed using Optimal Prime, a novel dedicated machine learning algorithm trained on the performance of thousands of primer sequences. LSV-seq achieves high on-target capture rates and concordance with RNA-seq, while requiring several-fold lower sequencing depth. We use LSV-seq to target events with low coverage in GTEx RNA-seq data and discover hundreds of previously hidden tissue-specific splicing events. Our results demonstrate the ability of LSV-seq to capture alternative splicing with exceptional sensitivity and highlight its potential to improve the detection of other RNA features of interest. Overall design: RNA-seq and LSV-seq of Jurkat cells (JSL1 clone) either left unstimulated or stimulated with PMA. Additionally, LSV-seq of human brain cerebellum, heart atrial appendage and liver tissues. Three replicates for each condition.