ABI Innovation: ProteoCarta: a community-scale resource for multi-species proteomics big data

ABI Innovation：ProteoCarta：多物种蛋白质组大数据的社区规模资源

• 批准号: 1759980
• 负责人: Nuno Bandeira
• 金额: $ 89.88万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1759980&HistoricalAwards=false
• 关键词: ABI; Innovation; ProteoCarta; community; scale

Proteomics research focuses on high throughput analysis of proteins, with the ultimate goal of understanding their role in the molecular biology of both simple and complex organisms. Following the successful development of mass spectrometry (MS) technologies, that have enabled the identification and quantification of many thousands of proteins in hundreds of species, the high throughput tandem mass spectrometry approach has become the method of choice for the overwhelming majority of large scale proteomics studies. Data acquisition has proceeding at faster and faster rates, and, combined with the reliability of large scale repositories (e.g., PRIDE, MassIVE, etc) and with journal publication guidelines for quality and annotation, the result has been public availability of hundreds of terabytes of well documented proteomics MS data. The total volume of proteomics data is currently doubling about every 1.5 years. There is tremendous potential for untapped discoveries lying in these 9000+ public datasets. Unfortunately the drive for sharing of raw data has not been accompanied by corresponding efforts to provide protein identification and abundance data, that are required for initial verification by MS experts and subsequent re-use by scientists who are not MS experts. As it stands today, less than 0.1% of all public non-human proteomics MS data only report identifications at the most minimal standards that show acceptable statistical controls; essentially no datasets report quantitative results. The ProteoCarta platform created through this award will address this problem by i) systematically reanalyzing public datasets using advanced computational mass spectrometry workflows and ii) organizing the results in a structured database so that results will be accessible through intuitive query interfaces.Building on computational MS platforms that have already processed over 10 billion spectra in 700,000 jobs from thousands of users in 122 countries, this proposal aims to address this community-scale challenge with a new ProteoCarta resource equipped to systematically reprocess proteomics MS big data from hundreds of species and designed to integrate all results into an open knowledge base that indexes repository-scale global proteomics data. ProteoCarta will significantly scale state of the art algorithms to enable analysis of hundreds of terabytes of data and will extend and integrate new spectral network algorithms for large scale discovery of protein variations within and across samples and species, including MS detection of sequence variants as well as discovery of unexpected or novel post-translational modifications. In addition to releasing all data and analysis results in standard open formats, ProteoCarta results will be directly attached to reanalyzed public datasets, to be stored at MassIVE, whereby hundreds of scientists will be automatically notified of the updates and directly encouraged to actively explore the new results on their own data. Links to resources will be provided through https://cseweb.ucsd.edu/~nbandeir/software.html and https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp .This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

蛋白质组学研究的重点是蛋白质的高通量分析，最终目标是了解它们在简单和复杂生物体的分子生物学中的作用。随着质谱（MS）技术的成功发展，已经能够鉴定和定量数百种物种中的数千种蛋白质，高通量串联质谱方法已经成为绝大多数大规模蛋白质组学研究的首选方法。数据采集以越来越快的速度进行，并且与大规模存储库的可靠性（例如，PRIDE，MassIVE等），并与期刊出版指南的质量和注释，结果已经公开提供了数百TB的有据可查的蛋白质组学MS数据。目前，蛋白质组学数据的总量大约每1.5年翻一番。在这9000多个公共数据集中，存在着巨大的未开发发现潜力。不幸的是，共享原始数据的驱动力并没有伴随着提供蛋白质鉴定和丰度数据的相应努力，这是MS专家进行初步验证和随后由非MS专家的科学家重新使用所需的。目前，所有公开的非人类蛋白质组学MS数据中，只有不到0.1%的数据仅报告了最低标准的鉴定结果，显示出可接受的统计控制;基本上没有数据集报告定量结果。通过该奖项创建的ProteoCarta平台将通过以下方式解决这一问题：i）使用先进的计算质谱工作流程系统地重新分析公共数据集; ii）将结果组织在结构化数据库中，以便通过直观的查询界面访问结果。该提案旨在通过一个新的ProteoCarta资源来解决这一社区规模的挑战，该资源配备有系统地重新处理来自数百个物种的蛋白质组学MS大数据，并旨在将所有结果整合到一个开放的知识库中，该知识库索引了储存库规模的全球蛋白质组学数据。 ProteoCarta将显著扩展最先进的算法，以分析数百TB的数据，并将扩展和整合新的光谱网络算法，用于大规模发现样品和物种内和跨样品和物种的蛋白质变异，包括序列变异的MS检测以及发现意外或新的翻译后修饰。除了以标准的开放格式发布所有数据和分析结果外，ProteoCarta的结果将直接附加到重新分析的公共数据集上，并存储在MassIVE中，数百名科学家将自动获得更新通知，并直接鼓励他们积极探索自己数据的新结果。资源链接将通过https://cseweb.ucsd.edu/~nbandeir/software.html和https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp提供。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 3.0

• DOI: 10.1021/acs.jproteome.9b00542
• 发表时间: 2019-12-01
• 期刊: JOURNAL OF PROTEOME RESEARCH
• 影响因子: 4.4
• 作者: Deutsch, Eric W.;Lane, Lydie;Omenn, Gilbert S.
• 通讯作者: Omenn, Gilbert S.

Index-based, High-dimensional, Cosine Threshold Querying with Optimality Guarantees

具有最优性保证的基于索引的高维余弦阈值查询

• DOI: 10.4230/lipics.icdt.2019.11
• 发表时间: 2019
• 期刊: Leibniz international proceedings in informatics
• 作者: Li, Y.;Wang, J.;Pullman, B.;Bandeira, N.;Papakonstantinou, Y.
• 通讯作者: Papakonstantinou, Y.

Open Science Resources for the Mass Spectrometry-Based Analysis of SARS-CoV-2

• DOI: 10.1021/acs.jproteome.0c00929
• 发表时间: 2021-02-19
• 期刊: JOURNAL OF PROTEOME RESEARCH
• 影响因子: 4.4
• 作者: Bittremieux, Wout;Adams, Charlotte;Bandeira, Nuno
• 通讯作者: Bandeira, Nuno