Computational approaches to delineate non-canonical splicing events

摘要： RNA剪接在RNA和蛋白质水平上产生巨大的变化，以调节细胞类型， 它具有特定的功能，也是许多疾病的原因。几类拼接 外显子跳跃、内含子保留和替代等模式已被广泛研究， 剪接位点测序技术的进步使得能够发现以前的 未知的非典型剪接事件。然而，由于缺乏专门的计算能力， 方法，这些非典型剪接的流行和功能后果 事件仍然未被探索。我们未来五年研究计划的目标是开发 新的和专门的计算算法的发现和表征新兴的 目前尚不充分研究的拼接模式。我们将集中在激子和非线性- 剪接转录本在我们提出的研究，因为这两个非典型剪接模型有 与最近研究报告的复杂人类疾病有关。我们将开发一个 一系列的算法，以（1）全面编目这些新的剪接模式，使用短 和长读段测序平台，（2）剖析非典型剪接的遗传基础 事件与深度转录组和全基因组测序数据的综合分析，以及 (3)阐明了新形式的RNA剪接改变的功能影响， 蛋白质组学策略我们提出的工作是创新的，因为我们将建立独特的 检测和表征新的非典型剪接事件的计算框架， 整合大型多组学数据集（例如TCGA、GTEx和ENCODE）。具有重要意义 因为它既可以应用于基础研究，以改善转录组注释， 潜在的基因组医学，以指导新的治疗策略的发展， 复杂的疾病。

Abstract: RNA splicing generates enormous variations at the RNA and protein levels to regulate cell-type- specific functions as well as being the cause for numerous diseases. Several classes of splicing patterns have been widely studied, such as exon skipping, intron retention and alternative splicing sites. Advances in sequencing technologies enable the discovery of previously unknown non-canonical splicing events. However, due to the lack of dedicated computational approaches, the prevalence and functional consequences of these non-canonical splicing events remain unexplored. The goal of our research program in the next five years is to develop novel and specialized computational algorithms for discovery and characterization of emerging splicing patterns that are currently understudied. We will focus on exitrons and non-linear- spliced transcripts in our proposed study, as these two non-canonical splicing models have been implicated in complex human diseases reported by recent studies. We will develop a series of algorithms to (1) comprehensively catalog these novel splicing patterns using short and long read sequencing platforms, (2) dissect the genetic basis of non-canonical splicing events with integrative analysis of deep transcriptome and whole-genome sequencing data, and (3) elucidate the functional impacts of novel forms of RNA splicing alternations using a proteogenomic strategy. Our proposed work is innovative in that we will build unique computational frameworks to detect and characterize novel non-canonical splicing events by integrating large multi-omics datasets (e.g. TCGA, GTEx and ENCODE). It is significant because it can be applied both in basic research to improve transcriptome annotation and potentially in genomic medicine to guide the development of novel therapeutic strategies for complex diseases.