RNA splicing analysis using heterogeneous and large RNA-seq datasets

The ubiquity of RNA-seq has led to many methods that use RNA-seq data to analyze variations in RNA splicing. However, available methods are not well suited for handling heterogeneous and large datasets. Such datasets scale to thousands of samples across dozens of experimental conditions, exhibit increased variability compared to biological replicates, and involve thousands of unannotated splice variants resulting in increased transcriptome complexity. We describe here a suite of algorithms and tools implemented in the MAJIQ v2 package to address challenges in detection, quantification, and visualization of splicing variations from such datasets. Here we created a large, realistic synthetic RNA-seq dataset of 150 simulated cerebellum samples and 150 skeletal muscle samples using BEERS. We use this as a benchmark dataset to assess the advantages of MAJIQ v2 compared to existing methods. Overall design: RNA-seq samples were simulated based on transcript expression profiles derived from publically available GTEx v8 transcript expression levels from 150 cerebellum and 150 skeletal muscle samples in order to perform differential splicing benchmarking analysis.