Brief Bioinform. 2019 Jul 19;20(4):1358-1375. doi: 10.1093/bib/bby005.
Alternative splicing (AS) has shown to play a pivotal role in the development of diseases, including cancer. Specifically, all the hallmarks of cancer (angiogenesis, cell immortality, avoiding immune system response, etc.) are found to have a counterpart in aberrant splicing of key genes. Identifying the context-specific regulators of splicing provides valuable information to find new biomarkers, as well as to define alternative therapeutic strategies. The computational models to identify these regulators are not trivial and require three conceptual steps: the detection of AS events, the identification of splicing factors that potentially regulate these events and the contextualization of these pieces of information for a specific experiment. In this work, we review the different algorithmic methodologies developed for each of these tasks. Main weaknesses and strengths of the different steps of the pipeline are discussed. Finally, a case study is detailed to help the reader be aware of the potential and limitations of this computational approach.
选择性剪接（AS）已被证明在疾病的发展中起着关键作用，包括癌症。具体来说，所有癌症的标志性特征（血管生成、细胞永生、逃避免疫系统反应等）都被发现与关键基因的异常剪接相对应。鉴定剪接的上下文特异性调节剂可提供有价值的信息，以寻找新的生物标志物，并定义替代治疗策略。识别这些调节剂的计算模型并不简单，需要三个概念步骤：AS 事件的检测、潜在调节这些事件的剪接因子的识别以及特定实验中这些信息的语境化。在这项工作中，我们回顾了为每个任务开发的不同算法方法。讨论了该流水线不同步骤的主要弱点和优势。最后，详细介绍了一个案例研究，以帮助读者了解这种计算方法的潜力和局限性。
