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

摘要 该研究计划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。绝大多数人类基因是交替拼接的 剪接的变异已被证明与复杂的疾病风险有关。尽管有广泛的传播 采用负担得起的高通量测序，RNA剪接的变异仍未得到充分研究 短读测序数据的局限性和与精确测序相关的计算挑战 基因表达的转录水平量化。我们建议开发改进检测的方法， 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，并描述剪接的机制 变异会影响复杂的性状。重要的是，我们的方法预测的变化和机制将是 在独立的队列中重复，并使用正交法进行实验验证。计算性的 我们将开发的方法和软件将应用于公开可用的数据和由我们的 组。我们建议不仅利用我们的专业知识，而且还利用我们现有的代码库和工具。这些工具将 支持单机和基于云的执行以进行纵向扩展分析，并将与现有 用于下游分析的工具。

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.