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: 9342975
• 负责人: Yi Xing
• 金额: $ 35.13万
• 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 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9342975
• 关键词: Address; Alcohol consumption; Alcohols; Alternative Splicing; Awareness; Big Data; Biological; Biological Phenomena; Biology; Biomedical Research; Brain; Caricatures; Cells; Code; Communities; Complement; Complex; Custom; Data; Data Analyses; Data Analytics; 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; Messenger RNA; Methods; Modality; Modeling; Names; Nerve Degeneration; Neurons; Open Reading Frames; Output; Oxidants; Pathogenesis; Peptides; Performance; Peripheral Blood Mononuclear Cell; Portraits; Protein Databases; Protein Isoforms; Proteins; Proteome; Proteomics; Public Domains; Quality Control; RNA; Rattus; Reading Frames; Regulation; Reporting; Research; Research Personnel; Resources; Ribosomes; Role; Sampling; Shotguns; Software Tools; Stress; Technology; Time; Transcript; Translating; Untranslated RNA; Vitronectin; cardiovascular risk factor; cell type; cloud based; computer framework; computerized tools; computing resources; design and construction; experimental study; falls; frontier; gene product; human disease; innovation; insight; mRNA Precursor; molecular sequence database; novel; oxidative damage; repository; 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.

项目摘要 在真核生物中，一个基因可以通过各种类型的替代性前体产生多种蛋白质同工型 mRNA加工（例如，替代剪接），显着导致蛋白质组的复杂性。微分 同工型表达在从心力衰竭到神经变性的疾病的发病机理中表现出来，以及 细胞对环境压力的反应，包括酒精和氧化损伤。 RNA-Seq的进步 技术导致发现了许多新型替代同工型，但它们的生物学影响通常是 在没有蛋白质信息的情况下不清楚。相反，shot弹枪蛋白质组学技术启用了大规模 蛋白质的表征，但“一基因，一生产”数据库的局限性禁止其实用性 蛋白质同工型鉴定。对替代同工型生物学的更深入的见解需要结合 完全具有转录组学和蛋白质组学的优势。彼此之间，技术平台的集成 从mRNA到蛋白质已成为推进基因产品的整体肖像的必不可少的一步。 关键挑战之一是蛋白质组学和转录组学存储库的分离，以及 断开相对数据分析管道和专业知识的连接。尽管最近取得了进展，但仍有紧急 对良好且用户友好的计算平台的需求未满足，这些平台可以支持每天生物医学 利用潜水员数据类型的研究人员用于多摩斯研究。 该项目的主要目标是创建一个统一的平台，以解码RNA-的替代同工型 Seq/Ribo-Seq数据，并指导shot弹枪蛋白质组学的蛋白质同工型表征。我们的方法 近期大数据科学的迅速革命，在多派中的新边界 现在集成使遍历异质计算资源和数据类型成为可能 无缝。我们将设计，构建和实施一个集成的蛋白质转让框架 （proteoseq），它将结合新颖的分析模型和自定义蛋白质组学工作流与结合 替代蛋白质同工型大规模特征的转录组学和蛋白质组学数据。我们的 提案详细介绍了三个数据科学的目的，这将（i）开发出推断全长mRNA和蛋白质的方法 来自混合（短阅读/长阅读）RNA-Seq和Ribo-Seq数据的同工型； （ii）工程师一个集成平台 用户可以从云上的蛋白质释放性数据中分析蛋白质同工型； （iii）验证和积累 潜水员高价值数据集中替代同工型的蛋白质证据。我们的努力旨在协同两个 目前，零碎的OMICS领域，从而促进有关替代同工型法规的询问 健康与疾病。我们设想提出的计算工具将可以推广到多个生物医学 学科，并将为广泛的科学界服务，以进行常规的多摩学调查 药品。