Computational approaches to delineate non-canonical splicing events

描述非规范剪接事件的计算方法

• 批准号: 10270575
• 负责人: Rendong Yang
• 金额: $ 39.75万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10270575
• 关键词: Algorithms; Alternative Splicing; Basic Science; Catalogs; Communities; Complex; Computational algorithm; Computer software; Data; Data Set; Development; Disease; Event; Genes; Genetic; Genomic medicine; Genotype-Tissue Expression Project; Goals; Health; Introns; Modeling; Molecular; Pattern; Population Analysis; Prevalence; Proteins; RNA; RNA Splicing; Reporting; Research; Series; Site; Technology; The Cancer Genome Atlas; Transcript; Variant; Work; cell type; computer framework; exon skipping; genome sequencing; human disease; improved; innovation; multiple omics; novel; novel therapeutic intervention; open source; programs; proteogenomics; sequencing platform; tool; transcriptome; whole genome

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.

抽象的： RNA剪接在RNA和蛋白质水平上产生巨大的变化来调节细胞类型 特定的功能，也是许多疾病的原因。拼接的几类 模式已被广泛研究，例如外显子跳跃、内含子保留和替代 剪接位点。测序技术的进步使得以前的发现成为可能 未知的非规范剪接事件。但由于缺乏专门的计算 方法、这些非规范剪接的普遍性和功能后果 事件仍有待探索。我们未来五年研究计划的目标是开发 用于发现和表征新兴事物的新颖且专门的计算算法 目前正在研究的拼接模式。我们将重点关注退出子和非线性 我们提出的研究中的剪接转录本，因为这两个非规范剪接模型具有 最近的研究报告称与复杂的人类疾病有关。我们将开发一个 一系列算法来（1）使用短文本对这些新颖的剪接模式进行全面分类 和长读长测序平台，（2）剖析非规范剪接的遗传基础 对深度转录组和全基因组测序数据进行综合分析的事件，以及 (3) 阐明新型 RNA 剪接改变的功能影响 蛋白质组策略。我们提出的工作是创新的，因为我们将建立独特的 用于检测和表征新颖的非规范剪接事件的计算框架 集成大型多组学数据集（例如 TCGA、GTEx 和 ENCODE）。意义重大 因为它既可以应用于基础研究以改善转录组注释， 可能在基因组医学中指导新治疗策略的开发 复杂的疾病。