Alternative splicing generates protein diversity that plays a critical role in disease pathogenesis and tumor development. To address this, we created OutSplice, a straightforward, accessible computational tool that detects splicing outliers in cancerous samples by benchmarking them against patterns observed in normal tissues. Although multiple existing software packages have been introduced to identify splicing anomalies, they differ considerably in their underlying methods, technical demands, and overall usability, often complicating tool selection for researchers new to the field. We therefore evaluated several such algorithms through systematic benchmarking to clarify their respective strengths and to illustrate how OutSplice complements or diverges from them. Results show that, while OutSplice flags fewer genes with statistically significant splicing aberrations, those it does identify tend to carry strong biological significance. Moreover, we uncovered 17 genes with consistent and prominent splicing changes in tumor samples that were independently detected by at least five of the tested tools, highlighting them as high-priority targets for subsequent investigation. In conclusion, the most effective strategy is to integrate OutSplice with complementary splicing analysis programs to confirm irregular events and prioritize those of greatest biological importance.
