Methods for RNA splicing variations detection, quantification, visualization, and association from large heterogeneous datasets

来自大型异构数据集的 RNA 剪接变异检测、量化、可视化和关联的方法

• 批准号: 9895303
• 负责人: Yoseph Barash
• 金额: $ 7.17万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2022-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9895303
• 关键词: Adoption; Affect; Alternative Splicing; Code; Complex; Computer software; Computing Methodologies; Data; Data Set; Detection; Disease; Event; Finding by Cause; Gene Expression; Genes; Genetic Variation; Goals; Heritability; High-Throughput Nucleotide Sequencing; Human; Imagery; Methods; Mutation; Patients; Persons; RNA; RNA Processing; RNA Splicing; Research; Research Personnel; Tissues; Transcript; Variant; Work; base; cell type; cloud based; cohort; disorder risk; genetic variant; human tissue; improved; programs; scale up; tool; trait; transcriptome sequencing

Abstract The goal of this research program is to develop methods and tools to analyze large heterogeneous RNA-seq data sets to better understand RNA splicing. The vast majority of human genes are alternatively spliced and variation in splicing has been shown to be associated with complex disease risk. Despite the wide spread adoption of affordable high throughput sequencing, variation in RNA splicing has remained understudied due to the limitations of short read sequencing data and the computational challenges associated with accurate transcript-level quantification of gene expression. We propose to develop methods to improve the detection, quantification, and visualization of complex splicing events. We will further develop methods to identify genetic variants associated with complex splicing variation and to characterize the mechanisms by which splicing variation affects complex traits. Importantly, the variations and mechanisms predicted by our methods will be replicated in independent cohorts and experimentally validated using orthogonal methods. The computational methods and software we will develop will be applied both to publicly available data and data generated by our groups. We propose to leverage not only our expertise but also our existing code base and tools. The tools will support both standalone and cloud based execution for scaling up analysis, and will integrate with existing tools for downstream analysis.

抽象的 该研究项目的目标是开发分析大型异质 RNA-seq 的方法和工具 数据集以更好地了解 RNA 剪接。绝大多数人类基因都是选择性剪接的 剪接变异已被证明与复杂疾病风险相关。尽管流传广泛 采用负担得起的高通量测序，RNA 剪接的变化仍然没有得到充分研究，因为 短读长测序数据的局限性以及与准确相关的计算挑战 基因表达的转录水平定量。我们建议开发方法来改进检测， 复杂剪接事件的量化和可视化。我们将进一步开发鉴定基因的方法 与复杂剪接变异相关的变体并表征剪接机制 变异影响复杂的性状。重要的是，我们的方法预测的变化和机制将是 在独立队列中复制并使用正交方法进行实验验证。计算性的 我们将开发的方法和软件将应用于公开数据和我们生成的数据 组。我们建议不仅利用我们的专业知识，而且利用我们现有的代码库和工具。这些工具将 支持独立和基于云的执行以扩大分析范围，并将与现有的集成 下游分析工具。