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异，以调节细胞类型-- 它具有独特的功能，也是导致多种疾病的原因。几种类型的拼接 模式已被广泛研究，如外显子跳过、内含子保留和替代 拼接位置。测序技术的进步使人们能够发现以前 未知的非规范剪接事件。然而，由于缺乏专门的计算能力 这些非规范剪接的途径、流行率和功能后果 这些事件仍未被探索。我们未来五年的研究计划的目标是发展 用于发现和表征新兴事物的新颖和专门的计算算法 目前研究不足的剪接模式。我们将重点关注激子和非线性- 我们建议的研究中的剪接转录，就像这两个非规范剪接模型 与最近研究报告的复杂人类疾病有牵连。我们将开发一种 一系列算法来(1)使用Short对这些新的剪接模式进行全面分类 和长读测序平台，(2)剖析非规范剪接的遗传基础 对深层转录组和全基因组测序数据进行综合分析的事件，以及 (3)阐明新形式的RNA剪接改变的功能影响使用 蛋白质基因组学战略。我们提议的工作是创新的，因为我们将建立独特的 通过以下方式检测和表征新型非正则剪接事件的计算框架 整合大型多组学数据集(如TCGA、GTEx和ENCODE)。这一点意义重大 因为它既可以应用于基础研究来改进转录组注释，也可以应用于基础研究 可能在基因组医学方面指导开发新的治疗策略 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.