Publication
JUM is a computational method for comprehensive annotation-free analysis of alternative pre-mRNA splicing patterns.
Wang Qingqing, Rio Donald C
# JUM: Annotation-Free Analysis of Alternative Splicing Patterns
Alternative pre-mRNA splicing (AS) is a fundamental process that expands transcriptomic and proteomic diversity in metazoans. Wang and Rio developed JUM (junction usage model), a computational method that identifies and quantifies global AS patterns directly from RNA-sequencing data without requiring pre-existing transcriptome annotations. This annotation-free approach overcomes limitations of existing methods that depend on incomplete reference libraries, particularly problematic for poorly annotated genomes or novel splice variants.
JUM employs a bottom-up strategy to detect five conventional AS patterns plus a "composite" category capturing complex pattern combinations. Notably, the method stringently classifies intron retention (IR)—a disease-relevant splicing pattern—with substantially reduced false-positive rates compared to annotation-dependent methods. The research was validated using RNA-sequencing samples from diverse sources including Drosophila and human cell lines, demonstrating the method's broad applicability for comprehensive AS profiling in systems where transcript annotations are incomplete or unavailable.
Abstract
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-sequencing data rely on preannotated 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 (IR), 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