BBSRC-NSF/BIO. Globally harmonized re-analysis of Data Independent Acquisition (DIA) proteomics datasets enables the creation of new resources

BBSRC-NSF/BIO。

• 批准号: BB/X001911/1
• 负责人: Juan Antonio Vizcaino
• 金额: $ 62.82万
• 依托单位: European Bioinformatics Institute
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX001911%2F1
• 关键词: BBSRC; NSF; BIO.; Globally; harmonized

Proteins are important molecules that carry out most of the activities that take place in each cell of an organism, such as transporting substances and providing structural support. A proteome is the complete set of all the proteins in a system or organism under certain conditions at a given time, and proteomics is the large-scale study of proteomes. Proteomics applies to many parts of biology as it can tell us a lot about how a system or organism works, and can provide vital information about illnesses and potential treatments.The main technique used in proteomics research is mass spectrometry (MS), which works by breaking up a mixed protein sample into small fragments, sorting them and then reporting their mass. This information is used to determine the identity and amount of the proteins. Recently, a MS approach called data independent acquisition (DIA) has become popular. Traditional MS, called data dependent acquisition (DDA), is biased towards the fragments that have the strongest signal, but DIA is not limited by this. This means that DIA allows researchers to quantify proteins that are present even in very small numbers, allowing for better representation of the proteome. Spectral libraries are collections of pre-annotated experimental MS outputs that are used in DIA data analysis. Recently spectral libraries have been developed using machine learning, which provides a great opportunity for novel artificial intelligence (AI) approaches to proteomics research. Overall, quantitative DIA data is very rich, as it represents a comprehensive digital record of the proteome that can be analysed using different tools and approaches over time.The groups involved in this project have been working to make DIA proteomics data freely available worldwide via the ProteomeXchange (PX) consortium, and to ensure that this data is generated and reported using consistent standards via the Proteomics Standards Initiative (PSI). This publicly-available data provides a great opportunity for researchers to reconfirm original results and obtain new insights. However, there have so far been very limited re-analysis efforts. This may be due to the complex nature of DIA data analysis, and also because of a lack of availability of spectral libraries.Our project aims to address this by generating new knowledge coming from the re-analysis of DIA proteomics datasets and creating novel infrastructure to better support public DIA proteomics data and spectral libraries. Additionally, we will create novel infrastructure for making spectral libraries Findable, Accessible, Interoperable and Re-usable (FAIR), which will enhance the reproducibility of published studies. To achieve these goals we will produce reliable and high-quality protein expression (i.e. protein production) and abundance information from the re-analysis of manually curated public DIA quantitative datasets and we will make these freely available in PX and via EMBL-EBI's Expression Atlas, to be consumed by non-experts in proteomics. We will also create protein co-expression and abundance maps for different biological conditions using the DIA re-analyses and make them available via PX. This would be the first time that these maps are generated on such large amounts of DIA proteomics data and will take advantage of the unique advantages, such as size and coverage, of DIA datasets. Further, we will develop novel infrastructure and data standards to make DIA proteomics data and, as a key point, spectral libraries FAIR. This will involve creating open source tools and infrastructure, and developing PSI standards.The co-expression maps, infrastructure and standards that will be generated by this project will benefit researchers across a wide range of biological and biomedical fields, and will provide the ability to strengthen and connect existing research findings. We will disseminate our work widely to train and assist researchers in making full use of these valuable resources.

蛋白质是重要的分子，它们在每个生物体的每个细胞中进行大多数活性，例如运输物质并提供结构支持。蛋白质组是在某些条件下在某些条件下在系统或生物体中所有蛋白质的完整集，而蛋白质组学是对蛋白质组的大规模研究。蛋白质组学适用于生物学的许多部分，因为它可以告诉我们有关系统或生物如何工作的方式，并可以提供有关疾病和潜在治疗的重要信息。蛋白质组学研究中使用的主要技术是质谱（MS），该技术通过将混合蛋白质样品分解为​​小碎片，将其分类并报告其质量来进行质谱（MS）。该信息用于确定蛋白质的身份和量。最近，一种称为数据独立获取（DIA）的MS方法已流行。传统的MS（称为数据依赖性获取（DDA））偏向具有最强信号的片段，但DIA并不受到此限制。这意味着DIA允许研究人员量化即使数量很少的蛋白质，从而可以更好地表示蛋白质组。光谱库是在DIA数据分析中使用的预注册实验MS输出的集合。最近，使用机器学习开发了光谱库，这为蛋白质组学研究提供了新的人工智能方法（AI）方法。总体而言，定量DIA数据非常丰富，因为它代表了蛋白质组的全面数字记录，可以随着时间的流逝进行不同的工具和方法进行分析。该项目所涉及的组一直在努力通过ProteOmeOmeXchange（PX）在全球范围内免费获得DIA蛋白质组学数据，并确保使用该数据来确保使用Protent Startaind Startaind Startarts Startaind Startaind Startaind Startaind Startaind stardard stardaind startaind startaind startaind strate takeard标准。这些公开可用的数据为研究人员提供了一个很好的机会，可以重新确认原始结果并获得新的见解。但是，到目前为止，重新分析工作非常有限。这可能是由于DIA数据分析的复杂性质，也是由于缺乏光谱库的可用性。我们的项目旨在通过从重新进行DIA蛋白质组学数据集的重新分析中产生新知识来解决这一问题，并创建新型基础架构以更好地支持公共DIA蛋白质组学数据和光谱图书馆。此外，我们将创建新的基础架构，以使光谱库可找到，可访问，可互操作和可重复使用（公平），从而增强已发表研究的可重复性。为了实现这些目标，我们将产生可靠和高质量的蛋白质表达（即蛋白质产生）和从重新进行手动策划的公共DIA定量数据集的重新分析中的丰度信息，我们将在PX和EMBL-EBI的表达地图集中免费获得这些数据集。我们还将使用DIA Re-Analyses为不同的生物条件创建蛋白质共表达和丰度图，并通过PX提供它们。这将是第一次在如此大的DIA蛋白质组学数据上生成这些地图，并将利用DIA数据集的独特优势，例如大小和覆盖范围。此外，我们将开发新颖的基础架构和数据标准，以制定DIA蛋白质组学数据，并作为关键点谱图公平。这将涉及创建开源工具和基础设施以及制定PSI标准。该项目将产生的共表达图，基础设施和标准将使跨广泛的生物学和生物医学领域的研究人员受益，并将提供增强和连接现有研究发现的能力。我们将广泛传播我们的工作，以培训和协助研究人员充分利用这些宝贵的资源。

项目成果

Expression Atlas update: insights from sequencing data at both bulk and single cell level.

• DOI: 10.1093/nar/gkad1021
• 发表时间: 2024-01-05
• 期刊: NUCLEIC ACIDS RESEARCH
• 影响因子: 14.9
• 作者: George, Nancy;Fexova, Silvie;Fuentes, Alfonso Munoz;Madrigal, Pedro;Bi, Yalan;Iqbal, Haider;Kumbham, Upendra;Nolte, Nadja Francesca;Zhao, Lingyun;Thanki, Anil S.;Yu, Iris D.;Marugan Calles, Jose C.;Erdos, Karoly;Vilmovsky, Liora;Kurri, Sandeep R.;Vathrakokoili-Pournara, Anna;Osumi-Sutherland, David;Prakash, Ananth;Wang, Shengbo;Tello-Ruiz, Marcela K.;Kumari, Sunita;Ware, Doreen;Goutte-Gattat, Damien;Hu, Yanhui;Brown, Nick;Perrimon, Norbert;Vizcaino, Juan Antonio;Burdett, Tony;Teichmann, Sarah;Brazma, Alvis;Papatheodorou, Irene
• 通讯作者: Papatheodorou, Irene