GRAPPA - Global compRehensive Atlas of Peptide and Protein Abundance

蛋白质是生物系统中携带功能的关键分子，作为催化反应的酶，作为信号转导器，允许细胞对变化的环境做出反应，并为细胞提供结构特征以及许多其他角色。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”方法得到广泛应用，使样本能够以高通量的方式进行分析。对于蛋白质的分析，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大量变化，以帮助了解疾病并潜在地开发治疗靶点。蛋白质组学技术依赖于昂贵的仪器和数据，收集和处理具有挑战性，包括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，并且在最初研究之外的不同应用中往往包含相当大的潜力。更广泛地说，在生物科学领域，已经朝着提高数据透明度和开放获取的方向发展，使结果能够得到验证，并扩大对研究的获取，超出了能够获得最佳技术的选定实验室。在蛋白质组学方面，申请人通过设计数据标准和开发免费可用的公共存储库，参与数据开放访问超过15年。现在的情况是，有大量的数据，特别是存放在EBI的PRIDE数据库中，这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ProteomeXchange数据库提供他们的数据，PRIDE是该数据库的主要成员。这些数据集经常被重新用于新的目的，例如支持定义基因在基因组中的位置或搜索蛋白质的修饰。然而，目前大多数蛋白质组学数据的再利用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，因此广泛学科的研究人员可以了解蛋白质在标准和不断变化的条件下（如疾病）如何分布在不同的细胞或组织中。我们在这个提案中的总体目标是建立数据分析管道，并重新处理已经在PRIDE和未来几年存储的100个数据集，这样我们就可以释放定量蛋白质组学数据中潜在的巨大未开发价值。数据将在一个新的“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，任何学科的研究人员都可以将数据可视化并批量下载，以便在任何下游应用程序中进行分析。

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.