GSE118193
Junction Usage Model (JUM) is a computational method for comprehensive annotation-free analysis of alternative pre-mRNA splicing patterns
Alternative pre-mRNA splicing (AS) greatly diversifies metazoan transcriptomes and proteomes and is crucial for gene regulation. Current computational analysis methods of AS from Illumina RNA-seq data rely on pre-annotated libraries of known spliced transcripts, which hinders AS analysis with poorly annotated genomes and can further mask unknown AS patterns. To address this critical bioinformatics problem, we developed a method called the Junction Usage Model (JUM) that uses a bottom-up approach to identify, analyze and quantitate global AS profiles without any prior transcriptome annotations. JUM accurately reports global AS changes in terms of the five conventional AS patterns and an additional "Composite" category composed of inseparable combinations of conventional patterns. JUM stringently classifies the difficult and disease-relevant pattern of intron retention, reducing the false positive rate of IR detection commonly seen in other annotation-based methods to near negligible rates. When analyzing AS in RNA-samples derived from Drosophila heads, human tumors and human cell lines bearing cancer-associated splicing factor mutations, JUM consistently identified ~ twice the number of novel AS events missed by other methods. Computational simulations showed JUM exhibits a 1.2-4.8 times higher true positive rate at a fixed cut-off of 5% false discovery rate. In summary, JUM provides a new framework and improved method that removes the necessity for transcriptome annotations and enables the detection, analysis and quantification of AS patterns in complex metazoan transcriptomes with superior accuracy. Overall design: Computationally simulated RNA-seq datasets that model the cellular conditions in Drosophila S2 cells when a splicing factor PSI is knocked down (testgroup2) versus the control condition when a non-targeting siRNA is transfected into the same cell line type (testgroup 1). Three levels of splicing changes were simulated (60% - testgroup2_0.8; 40% - testgroup2_0.6; 20%-testgroup2_0.4). Three replicates were simulated for each condition.