ProteoSeq - An Integrative Computational Framework for Proteotranscriptomics

ProteoSeq - 蛋白质转录组学的综合计算框架

• 批准号: 9193233
• 负责人: Yi Xing
• 金额: $ 36.5万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9193233
• 关键词: Address; Alcohol consumption; Alcohols; Alternative Splicing; Big Data; Biological; Biological Phenomena; Biology; Biomedical Research; Brain; Caricatures; Cells; Code; Communities; Complement; Complex; Custom; Data; Data Analyses; Data Science; Data Set; Data Sources; Databases; Deposition; Discipline; Disease; Engineering; Eukaryota; Event; Genes; Goals; Health; Heart; Heart Diseases; Heart failure; Human; Hybrids; Imagery; Internet; Investigation; Joints; Knowledge; Language; Learning; Length; Libraries; Malignant Neoplasms; Mass Spectrum Analysis; Messenger RNA; Methods; Modality; Modeling; Names; Nerve Degeneration; Neurons; Open Reading Frames; Output; Oxidants; Pathogenesis; Peptides; Performance; Peripheral Blood Mononuclear Cell; Portraits; Process; Protein Databases; Protein Isoforms; Proteins; Proteome; Proteomics; Public Domains; Quality Control; RNA; Rattus; Reading; Reading Frames; Regulation; Reporting; Research; Research Personnel; Resources; Ribosomes; Role; Sampling; Shotguns; Software Tools; Stress; Technology; Time; Transcript; Translating; Untranslated RNA; cardiovascular risk factor; cell type; cloud based; computer framework; computerized tools; computing resources; design and construction; empowered; falls; frontier; gene product; human disease; innovation; insight; mRNA Precursor; novel; oxidative damage; repository; research study; response; ribosome profiling; segregation; tool; transcriptome sequencing; transcriptomics; translational medicine; user-friendly

PROJECT SUMMARY In eukaryotes, one gene can give rise to multiple protein isoforms through various types of alternative pre- mRNA processing (e.g., alternative splicing), contributing significantly to proteome complexity. Differential isoform expression manifests in pathogenesis of diseases from heart failure to neurodegeneration, as well as cellular responses to environmental stress including alcohol and oxidative damage. Advances in RNA-seq technology have led to the discovery of many novel alternative isoforms, but their biological impact is often unclear in the absence of protein information. Conversely, shotgun proteomics technology enables large-scale characterization of proteins, but the limitations of “one-gene, one-product” databases prohibit their utility in protein isoform identification. Deeper insights into the biology of alternative isoforms require combining the complementary strengths of transcriptomics and proteomics. Accordingly, the integration of technical platforms from mRNA to protein has become an indispensable step in advancing a holistic portrait on gene products. Among the key challenges is the segregation of proteomics and transcriptomics repositories, as well as the disconnect of respective data analysis pipelines and expertise. Despite recent progress, there is an urgent and unmet need for well-integrated and user-friendly computational platforms that can support everyday biomedical researchers in harnessing diverse data types for multi-omics studies. The central goal of this project is to create a unified platform to decode alternative isoforms from RNA- seq/Ribo-seq data, and to guide shotgun proteomics characterization of protein isoforms. Our approach capitalizes on the rapid revolution of Big Data sciences in recent times, where new frontiers in multi-omics integration now make it possible to traverse heterogeneous computational resources and data types seamlessly. We will design, construct, and implement an integrative proteotranscriptomics framework (ProteoSeq), which will combine novel analytical models and custom proteomics workflows to coalesce transcriptomics and proteomics data for large-scale characterizations of alternative protein isoforms. Our proposal details three data science aims, which will (i) develop methods to infer full-length mRNA and protein isoforms from hybrid (short-read/long-read) RNA-seq and Ribo-seq data; (ii) engineer an integrative platform for users to analyze protein isoforms from proteotranscriptomics data on the cloud; and (iii) validate and accrue protein evidence for alternative isoforms in diverse high-value datasets. Our efforts aim to synergize two currently fragmentary omics fields and thereby empower inquiries on the regulations of alternative isoforms in health and disease. We envision the proposed computational tools will be generalizable to multiple biomedical disciplines, and will serve the broad scientific community for routine multi-omics investigations in translational medicine.

项目概要 在真核生物中，一个基因可以通过各种类型的替代前体产生多种蛋白质亚型。 mRNA 加工（例如选择性剪接）对蛋白质组复杂性有显着贡献。微分 同工型表达体现在从心力衰竭到神经退行性疾病的发病机制中，以及 细胞对环境压力（包括酒精和氧化损伤）的反应。 RNA 测序的进展 技术已经导致许多新的替代异构体的发现，但它们的生物学影响往往是 在缺乏蛋白质信息的情况下不清楚。相反，鸟枪法蛋白质组学技术能够实现大规模 蛋白质的表征，但“单一基因、单一产物”数据库的局限性阻碍了它们在 蛋白质亚型鉴定。更深入地了解替代异构体的生物学需要结合 转录组学和蛋白质组学的优势互补。相应地，技术平台的整合 从mRNA到蛋白质已成为推进基因产品整体描绘不可或缺的一步。 主要挑战之一是蛋白质组学和转录组学存储库的分离，以及 各自数据分析管道和专业知识的脱节。尽管最近取得了进展，但仍有一个紧迫且 对能够支持日常生物医学的集成良好且用户友好的计算平台的需求尚未得到满足 研究人员利用不同的数据类型进行多组学研究。 该项目的中心目标是创建一个统一的平台来解码 RNA 中的替代亚型 seq/Ribo-seq 数据，并指导蛋白质亚型的鸟枪法蛋白质组学表征。我们的方法 利用近年来大数据科学的快速革命，多组学的新领域 集成现在可以遍历异构计算资源和数据类型 无缝地。我们将设计、构建和实施综合蛋白质转录组学框架 （ProteoSeq），它将结合新颖的分析模型和定制蛋白质组学工作流程来合并 用于大规模表征替代蛋白质亚型的转录组学和蛋白质组学数据。我们的 提案详细说明了三个数据科学目标，其中将 (i) 开发推断全长 mRNA 和蛋白质的方法 来自混合（短读长/长读长）RNA-seq 和 Ribo-seq 数据的亚型； (ii) 设计一个综合平台 供用户从云上的蛋白质转录组数据中分析蛋白质亚型； (iii) 验证并累积 不同高价值数据集中替代亚型的蛋白质证据。我们的努力旨在协同两个 目前组学领域支离破碎，从而能够对替代异构体的监管进行查询 健康和疾病。我们预计所提出的计算工具将可推广到多种生物医学领域 学科，并将为广泛的科学界服务，进行转化中的常规多组学研究 药品。