ASTA-P: a pipeline for the detection, quantification and statistical analysis of complex alternative splicing events

Alternative splicing dramatically increases the repertoire of the human transcriptome and plays a critical role in cellular differentiation. Long read sequencing has dramatically improved our ability to explore isoform diversity directly. However, short read sequencing still provides advantages in terms of sequencing depth at low cost, which is important in comparative quantitative studies. Here, we present a pipeline called ASTA-P for profiling, quantification, and differential splicing analysis of tissue-specific, arbitrarily complex alternative splicing patterns. We discover novel events by supplementing existing annotation with reconstructed transcripts and use spliced RNA-seq reads to quantify splicing changes accurately based on their unique assignments. We used simulated RNA-seq data to demonstrate that ASTA-P provides a good trade-off between discovery and accuracy compared with several popular methods. Further, we applied ASTA-P to analyse AS patterns in real data from hiPSC derived cranial neural crest cells capturing the transition from primary neural cells into migratory cranial neural crest cells, differentiated by their expression of the transcription factor, SOX10. Our analysis revealed a significant splicing complexity, i.e., numerous AS events that cannot be described using the conventionally analysed 2D splicing event patterns. Such events are misclassified when analysed using current differential splicing analysis methods. Thus, ASTA-P provides a new approach for studying both conventional and complex splicing across different cellular conditions and the dynamic regulation of AS. The pipeline is available at https://github.com/uqktiwar/ASTAP/tree/main