GRAPPA - Global compRehensive Atlas of Peptide and Protein Abundance

GRAPPA - 全球肽和蛋白质丰度综合图谱

• 批准号: BB/T019557/1
• 负责人: Andrew Jones
• 金额: $ 41.84万
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FT019557%2F1
• 关键词: GRAPPA; Global; compRehensive; Atlas; Peptide

Proteins are the key molecules in biological systems carrying functions, acting as enzymes to catalyse reactions, as a signalling transducers to allow cells to respond to changing environments and providing structural features to cells amongst many other roles. Biological and biomedical research has undergone a technology revolution in recent decades, whereby "Big Data" approaches have become widespread, enabling samples to be analysed in a high-throughput manner. For analysis of proteins, a suite of technologies collectively called "proteomics", use mass spectrometry to measure 1000s of proteins simultaneously, enabling researchers to study which groups of proteins change in abundance during, for example, disease processes to help understand the disease and potentially develop therapeutic targets. Proteomics techniques rely upon expensive instrumentation and data are challenging to collect and process, including complex protocols for lab analysis and downstream data/statistical analysis. As a result, data are potentially highly valuable and very often contain considerable potential for different applications beyond the initial study. More generally in biosciences, there has been a move towards greater transparency and open access of data, enabling results to be validated and to widen access to research, beyond select labs with access to the best technology. In proteomics, the applicants have been involved with making data open access for over 15 years, through the design of data standards and developing freely available public repositories. The situation now is one where there are vast amounts of data, particularly deposited in the EBI's PRIDE database, the world leading resource for proteomics datasets. Most relevant journals publishing studies require that authors make their data available, through an umbrella collection of databases called ProteomeXchange, of which PRIDE is the leading member. These datasets are routinely re-used for new purposes, for example to support defining where genes exist in genomes or to search for modifications to proteins. However, most of the re-use of proteomics data is currently done by specialist research groups with expertise themselves in proteomics data analysis. PRIDE mostly contains raw data as collected of the instrument (prior to complex processing) or lists of proteins that have been identified, but not quantitative values represented in a standard way. Quantitative measurements of proteins are potentially highly valuable so that researchers in a wide range of disciplines can understand how proteins are distributed in different cells or tissues under standard and changing conditions (such as diseases). Our overall goal in this proposal is to build data analysis pipelines and reprocess 100s of datasets already in PRIDE and those deposited in the coming years, so that we can unlock this potentially huge untapped value in quantitative proteomics data. The data will be represented in a new "PRIDE Quant" module, and passed via a pipeline to the EBI's Expression Atlas database, which is designed to present a "biologist-friendly" view of the data, where researchers from any discipline can visualise data and download it in large batches for analysis in any downstream application.

蛋白质是携带功能的生物系统中的关键分子，充当催化反应的酶，作为信号传导传感器，使细胞能够响应不断变化的环境并为细胞提供许多其他角色的结构特征。近几十年来，生物学和生物医学研究已经进行了一场技术革命，在这种革命中，“大数据”方法已变得广泛，从而可以以高通量方式对样本进行分析。为了分析蛋白质，一套统称为“蛋白质组学”的技术套件，使用质谱法同时测量1000秒的蛋白质，使研究人员能够研究哪些蛋白质群体在疾病过程中的蛋白质群体变化，例如，疾病过程以帮助了解疾病并潜在地发展治疗靶标。蛋白质组学技术依赖于昂贵的仪器，并且数据在收集和处理方面具有挑战性，包括用于实验室分析和下游数据/统计分析的复杂协议。结果，数据具有很高的价值，并且通常在初始研究以外的不同应用中包含巨大的潜力。在生物科学方面，更一般而言，已经朝着更高的透明度和开放访问数据访问迈进，从而使结果得到验证并扩大了对研究的访问，超越了可以访问最佳技术的精选实验室。在蛋白质组学方面，申请人通过设计数据标准并开发免费的公共存储库，参与了15年以上的数据开放访问。现在的情况是有大量数据，特别是存放在EBI的Pride数据库中的数据，这是蛋白质组学数据集的世界领先资源。大多数相关的期刊发布研究要求作者通过称为ProteOmeXchange的数据库收集的伞，使他们的数据可用，其中Pride是领先的成员。这些数据集通常用于新目的，例如支持定义基因中存在基因或对蛋白质进行修饰的位置。但是，大多数蛋白质组学数据的重复使用目前是由具有蛋白质组学数据分析专业知识的专业研究小组完成的。 Pride主要包含仪器收集的原始数据（在复杂处理之前）或已鉴定出的蛋白质列表，但没有以标准方式表示的定量值。蛋白质的定量测量可能具有很高的价值，因此，在标准和变化的条件下（例如疾病），蛋白质中的研究人员可以了解蛋白质如何分布在不同细胞或组织中。我们在此提案中的总体目标是建立数据分析管道和重新处理的100个数据集和未来几年存放的数据集，以便我们可以在定量蛋白质组学数据中解锁这种潜在的巨大未开发的价值。数据将以新的“ Pride Quant”模块表示，并通过管道传递到EBI的表达地图集数据库，该数据库旨在呈现数据的“生物学家友好友好”视图，其中任何学科的研究人员都可以在任何下游应用程序中可视化数据并大量下载其大量批次。

项目成果

Assessing Multiple Evidence Streams to Decide on Confidence for Identification of Post-Translational Modifications, within and Across Data Sets

• DOI: 10.1101/2022.12.15.520504
• 发表时间: 2022-12
• 期刊: Journal of Proteome Research
• 影响因子: 4.4
• 作者: O. Camacho;Kerry A Ramsbottom;Andrew Collins;A. Jones

Is DIA proteomics data FAIR? Current data sharing practices, available bioinformatics infrastructure and recommendations for the future.

• DOI: 10.1002/pmic.202200014
• 发表时间: 2023-04
• 期刊: Proteomics
• 影响因子: 3.4

Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues

• DOI: 10.1101/2021.12.20.473413
• 发表时间: 2022-05-03
• 期刊: bioRxiv
• 作者: Wang，S.;Garcia-Seisdedos，D.;Vizcaino，J. A.
• 通讯作者: Vizcaino，J. A.

Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues.

• DOI: 10.1371/journal.pcbi.1010174
• 发表时间: 2022-06
• 期刊: PLoS computational biology
• 影响因子: 4.3

Assessing Multiple Evidence Streams to Decide on Confidence for Identification of Post-Translational Modifications, within and Across Data Sets.

• DOI: 10.1021/acs.jproteome.2c00823
• 发表时间: 2023-06-02
• 期刊: JOURNAL OF PROTEOME RESEARCH
• 影响因子: 4.4
• 作者: Camacho, Oscar M.;Ramsbottom, Kerry A.;Collins, Andrew;Jones, Andrew R.
• 通讯作者: Jones, Andrew R.