Leveraging the Cloud for Splicing Discovery

利用云进行拼接发现

• 批准号: 10405789
• 负责人: Nathan G. Salomonis
• 金额: $ 23.85万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10405789
• 关键词: Address; Adult; Algorithms; Alternative Splicing; Binding Sites; Bioinformatics; Cancer Vaccines; Cause of Death; Cell Line; Cells; Child; Computer software; Data; Data Analyses; Data Set; Deposition; Disease; Docking; Documentation; Environment; Eukaryota; Event; Exons; Frequencies; Funding; Genotype-Tissue Expression Project; Grant; Human; Immunotherapy; Introns; Language; Malignant Childhood Neoplasm; Malignant Neoplasms; Manuals; Messenger RNA; Methods; MicroRNAs; Pathway interactions; Patients; Privatization; Process; Protein Isoforms; Protein Structure Initiative; Proteomics; Protocols documentation; Publishing; Pythons; RNA Splicing; Research Personnel; Role; Running; Sampling; Secure; Spliceosomes; Supervision; System; Tertiary Protein Structure; The Cancer Genome Atlas; Time; Tissues; Training; Translating; United States National Institutes of Health; Update; Vaccines; Variant; Visualization; Work; analysis pipeline; bioinformatics tool; cancer immunotherapy; cancer subtypes; cloud based; computing resources; cost; design; experience; human RNA sequencing; improved; neoantigens; novel; open source; parallelization; patient health information; patient subsets; research study; single cell analysis; terabyte; tool; transcriptome; transcriptome sequencing; web interface; web site

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.

项目概要： 选择性剪接是高级真核生物中蛋白质组多样性的最重要贡献者之一。 当癌症中受到破坏时，错误剪接会产生健康细胞中未观察到的独特 mRNA 亚型。 癌症特异性剪接亚型代表了针对靶向癌症的潜在新抗原的未开发库 作为我们资助的 NCI R01“公正识别”的核心组成部分。 跨癌症的剪接体脆弱性”，我们一直在扩展和利用全面的剪接 分析管道来定义人类癌症和健康组织的剪接漏洞。 通过这项工作建立的生物信息学工具旨在识别已知和新型癌症 亚型，指定关键的调控剪接因子，推断功能性剪接亚型影响并发现癌症- 可以通过新兴免疫疗法或疫苗利用的特定新抗原生物信息学工具。 产生的这些发现与大型 AltAnalyze 开源项目的不同组成部分基本一致， 2008 年开始。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 历史上，在 TCGA、GTEx 和其他大型 RNA-Seq 纲要的规模上应用此工作流程 需要大量的计算资源和时间来下载和重新处理数百 TB 的数据 新兴的基于云的解决方案有可能减轻数据的影响。 这些技术挑战是通过以低成本对数千个预处理样本进行简化计算来实现的。 为了应对这些挑战，我们建议： 目标 1：针对云简化和优化 AltAnalyze 在此目标中，我们将解耦并优化 AltAnalyze。 AltAnalyze 的主要剪接分析组件可实现简化的监督和无监督 癌症转录组分析将打包为 CWL 管道，并使用 Docker 进行容器化。 并存入DockStore，可在云端实现快速、全面的拼接分析。 目标 2：将 AltAnalyze.cloud 集成到 Terra.bio 中以实现受控访问序列的直接分析。 TCGA、TARGET、GTEx 和其他主要人类 RNA 测序数据集中的 level 文件，我们将 1) 翻译我们的 CWL 工作流转为工作流描述语言 (WDL)，2) 建立集成的 Terra 工作流 使用 AltAnalyze.cloud 的拼接分析将能够通过 Terra Web 运行。 用于分析、进度跟踪、来源和结果共享的界面将启用这些功能。 简化了用户和受控 NIH 在云中存储的数据集的重复使用。