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.

蛋白质是生物系统中承载功能的关键分子，充当催化反应的酶，作为信号转导器使细胞能够响应不断变化的环境，并为细胞提供结构特征等许多其他作用。近几十年来，生物和生物医学研究经历了一场技术革命，“大数据”方法变得普遍，使得样本能够以高通量的方式进行分析。对于蛋白质分析，一套统称为“蛋白质组学”的技术使用质谱法同时测量数千种蛋白质，使研究人员能够研究哪些蛋白质组在疾病过程中发生丰度变化，以帮助了解疾病和潜在的变化。制定治疗目标。蛋白质组学技术依赖于昂贵的仪器，并且数据的收集和处理具有挑战性，包括用于实验室分析和下游数据/统计分析的复杂协议。因此，数据具有潜在的高度价值，并且通常在初始研究之外的不同应用中包含巨大的潜力。更普遍的是，在生物科学领域，已经出现了提高数据透明度和开放获取数据的趋势，从而使结果得到验证并扩大研究范围，而不仅仅是能够获得最佳技术的精选实验室。在蛋白质组学领域，申请人通过数据标准设计和开发免费公共存储库，致力于数据开放获取超过 15 年。现在的情况是存在大量数据，特别是存储在 EBI 的 PRIDE 数据库中，该数据库是世界领先的蛋白质组学数据集资源。大多数发表研究的相关期刊都要求作者通过名为 ProteomeXchange 的伞式数据库集合提供数据，PRIDE 是该数据库的主要成员。这些数据集通常会被重新用于新的目的，例如支持定义基因在基因组中存在的位置或搜索蛋白质的修饰。然而，目前蛋白质组数据的大部分重复利用是由在蛋白质组数据分析方面具有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）或已识别的蛋白质列表，但不包含以标准方式表示的定量值。蛋白质的定量测量具有潜在的高度价值，因此各个学科的研究人员可以了解蛋白质在标准和变化的条件（例如疾病）下如何分布在不同的细胞或组织中。我们在该提案中的总体目标是建立数据分析管道并重新处理 PRIDE 中已有的以及未来几年存放的数百个数据集，以便我们能够释放定量蛋白质组学数据中潜在的巨大未开发价值。数据将在新的“PRIDE Quant”模块中表示，并通过管道传递到 EBI 的 Expression Atlas 数据库，该数据库旨在呈现“生物学家友好”的数据视图，来自任何学科的研究人员都可以在其中可视化数据并大批量下载它以便在任何下游应用程序中进行分析。

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.