Leveraging the Cloud for Splicing Discovery

项目概要： 选择性剪接是高等真核生物蛋白质组多样性的重要组成部分。 当在癌症中被破坏时，错误剪接导致在健康细胞中未观察到的独特mRNA同种型。等 癌症特异性剪接异构体代表了靶向癌症的潜在新抗原的未开发库 疫苗和免疫疗法。作为我们资助的NCI R 01的核心组成部分， 癌症中的剪接体脆弱性”，我们一直在扩展和利用一个全面的剪接 分析管道，以定义跨人类癌症和健康组织的剪接脆弱性。相关联的 通过这一努力建立的生物信息学工具旨在识别已知和新的癌症 亚型，提名关键的调节剪接因子，推断功能性剪接异构体的影响和发现癌症- 新出现的免疫疗法或疫苗可以利用特异性新抗原。生物信息学工具 为了产生这些发现，这些发现与大型AltAnalyze开源项目的不同组件基本一致， 始于2008年。虽然AltAnalyze或其算法已在400多项已发表的研究中被引用，但重新 在TCGA、GTEx和其他大型RNA-Seq纲要的规模上应用这种工作流程， 需要大量的计算资源和时间来下载和重新处理数百TB的 以安全和兼容的方式存储数据。新兴的基于云的解决方案有可能缓解 通过以低成本对数千个预处理样本进行简化计算，解决这些技术挑战。 为应对这些挑战，我们建议： 目标1：简化和优化AltAnalyze云计算。在这个目标中，我们将解耦和优化 AltAnalyze的主要拼接分析组件，以实现简化的监督和非监督 癌症转录组的分析。AltAnalyze将打包为CWL管道，并使用Docker容器化 并存储在DockStore中，从而能够在云中快速全面地分析拼接。 目标2：将AltAnalyze.cloud集成到Terra.bio中。为了能直接分析控制访问序列 TCGA、TARGET、GTEx和其他主要的人类RNA测序数据集的水平文件，我们将1）翻译我们的 CWL工作流到工作流描述语言（WDL）和2）建立一个Terra工作流集成 使用AltAnalyze.cloud进行拼接分析。AltAnalyze.cloud将能够通过Terra网络运行 用于分析、进度跟踪、来源和成果共享的接口。这些功能将使 简化了用户和受控NIH在云中存储的数据集的重用。

PROJECT SUMMARY: Alternative splicing is among the most important contributors of proteomic diversity in higher order eukaryotes. When disrupted in cancer, mis-splicing results in unique mRNA isoforms not observed in healthy cells. Such cancer-specific splice isoforms represent an untapped reservoir of potential neoantigens for targeted cancer vaccines and immunotherapies. As a central component of our funded NCI R01 for “Unbiased identification of spliceosome vulnerabilities across cancer”, we have been extending and leveraging a comprehensive splicing analysis pipeline to define splicing vulnerabilities across human cancers and healthy tissues. The associated bioinformatics tools that are built-upon through this effort are designed to identify both known and novel cancer subtypes, nominate key regulatory splicing factors, infer functional splice-isoform impacts and discover cancer- specific neoantigens that can be exploited by emerging immunotherapies or vaccines. The bioinformatics tools to yield these discoveries largely consistent of distinct components of the large AltAnalyze open-source project, begun in 2008. While AltAnalyze or its algorithms have been cited in over 400 published research studies, re- applying this workflow at the scale of TCGA, GTEx and other large RNA-Seq compendiums have historically required significant computational resources and time to download and re-process hundreds of terabytes of data in a secure and compliant manner. New emerging cloud-based solutions have the potential to mitigate these technical challenges through streamlined compute of thousands of pre-processed samples at a low cost. To address these challenges, we propose to: Aim 1: Streamline and optimize AltAnalyze for the cloud. In this aim, we will decouple and optimize the primary splicing analysis components of AltAnalyze to enable streamlined supervised and unsupervised analysis of cancer transcriptomes. AltAnalyze will be packaged as a CWL pipeline, containerized with Docker and deposited in DockStore, enabling fast and comprehensive analyses of splicing in the cloud. Aim 2: Integrate AltAnalyze.cloud in Terra.bio. To enable direct analyses of controlled-access sequence- level files in TCGA, TARGET, GTEx and other major human RNA-sequencing datasets, we will 1) translate our CWL workflows to the Workflow Description Language (WDL) and 2) establish a Terra workflow for integrated splicing analysis using AltAnalyze.cloud. AltAnalyze.cloud will be able to be run through the Terra web interface for analysis, progress tracking, provenance and sharing of results. These features will enable streamlined re-use of user and controlled NIH deposited datasets in the cloud.