ABSTRACT

Most of the current alternative splicing (AS) analysis tools are powerless to analyse complex splicing. To address this, we developed SUVA (Splice sites Usage Variation Analysis) that decomposes complex splicing events into five types of splice junction pairs. By analysing real and simulated data, SUVA showed higher sensitivity and accuracy in detecting AS events than the compared methods. Notably, SUVA detected extensive complex AS events and screened out 69 highly conserved and dominant AS events associated with cancer. The cancer-associated complex AS events in FN1 and the co-regulated RNA-binding proteins were significantly correlated with patient survival.

摘要

目前大多数选择性剪接（AS）分析工具对复杂剪接分析无能为力。为了解决这个问题，我们开发了 SUVA（剪接位点使用变化分析），将复杂的剪接事件分解为五种类型的剪接点对。通过分析真实数据和模拟数据，SUVA 在检测 AS 事件方面表现出比比较方法更高的灵敏度和准确性。值得注意的是，SUVA 检测到广泛的复杂 AS 事件，并筛选出 69 个与癌症相关的高度保守和显性的 AS 事件。FN1 中的癌症相关复合物 AS 事件和共同调节的 RNA 结合蛋白与患者存活率显着相关。