ProteoSeq - An Integrative Computational Framework for Proteotranscriptomics

项目总结 在真核生物中，一个基因可以通过不同类型的前置替换蛋白产生多种蛋白质亚型。 信使核糖核酸的加工(例如，选择性剪接)，大大增加了蛋白质组的复杂性。差动 异构体表达在从心力衰竭到神经退行性疾病的发病机制中以及 细胞对环境压力的反应，包括酒精和氧化损伤。RNA-SEQ的研究进展 技术导致了许多新的替代亚型的发现，但它们的生物学影响通常是 在缺乏蛋白质信息的情况下不清楚。相反，鸟枪式蛋白质组学技术使大规模 蛋白质的特性，但“一个基因，一个产品”数据库的局限性限制了它们在 蛋白质亚型鉴定。要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 从信使核糖核酸到蛋白质，已成为推进基因产品整体画像不可或缺的一步。 关键挑战之一是蛋白质组学和转录组信息库的分离，以及 切断各自的数据分析渠道和专业知识。尽管最近取得了进展，但仍有一个紧急和 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。 这个项目的中心目标是创建一个统一的平台来解码来自RNA的替代亚型- SEQ/RIBO-SEQ数据，并指导鸟枪式蛋白质组学对蛋白质异构体的表征。我们的方法 利用近年来大数据科学的快速革命，在这些革命中，多组学的新前沿 集成现在使遍历不同的计算资源和数据类型成为可能 天衣无缝。我们将设计、构建和实现一个整合的蛋白质转录组学框架 (ProteoSeq)，它将结合新的分析模型和定制的蛋白质组工作流，以合并 转录组学和蛋白质组学数据，用于大规模描述可替代的蛋白质异构体。我们的 该提案详细说明了三个数据科学目标，这三个目标将(I)开发推断全长mRNA和蛋白质的方法 来自混合(短读/长读)RNA-seq和ribo-seq数据的异构体；(Ii)设计一个综合平台 供用户在云上分析蛋白质转录数据中的蛋白质异构体；以及(Iii)验证和积累 在不同的高价值数据集中发现替代亚型的蛋白质证据。我们的努力旨在使两个 目前支离破碎的组学领域，从而使对替代亚型的调控的查询成为可能 健康和疾病。我们设想所提出的计算工具将被推广到多种生物医学 学科，并将为广泛的科学界在翻译的常规多组学研究服务 医药。

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.