A curated, publically-accessible database of protein nanoscale organisation
A curated, publically-accessible database of protein nanoscale organisation
批准号:
BB/X018644/1
负责人:
Dylan Owen
金额:
$72.75万
依托单位:
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
项目状态:
未结题
起止时间:
2023 至 --
中文摘要
目前对蜂窝系统的描述是不完整的。它们可以在基因组、转录组和蛋白质组水平上进行表征,但还有一个最终水平:这些蛋白质如何在3D空间中组织。由于超分辨率显微镜的出现,特别是单分子成像(SMLM),科学家现在可以获得这些信息,它允许以纳米精度绘制生物分子的位置。精心策划的、可公开访问的数据库在生物学领域具有变革性。建立良好的数据库,如基因库,被广泛使用,但它们包含的数据相对简单。更复杂的数据集包括蛋白质结构数据库,例如PDB和来自DeepMind的AlphaFold 2的相关预测结构。该应用程序建立在最初的艾伦图灵研究所,EPSRC和BBSRC投资的基础上,旨在成为存储,共享,管理和处理SMLM数据的国家和全球资源。一旦建立,我们将为一个新的组学领域,纳米组学奠定基础:蛋白质纳米级组织的研究。最终,该资源将成为一个数据库,用户可以存储,共享和传播他们的SMLM数据,有利于公众参与科学,促进合作,并帮助满足资助者和出版商的数据共享要求。社区管理结构将确保数据库遵循研究道德和科学卓越的最佳实践。该数据库还将配备先进的数据分析工具,运行在云端,允许用户从上传的数据集中提取生物相关信息。这有助于为那些没有手段的人带来先进的统计分析,并有助于使先进的成像技术民主化。最后,我们将对上传数据的荟萃分析进行初步研究,并开创纳米组学的新领域-研究蛋白质,细胞和生物体之间蛋白质纳米级组织的多样性。
英文摘要
Current descriptions of cellular systems are incomplete. They can be characterised at the genomic, transcriptomic and proteomic level, but there is a final level: How those proteins are organised in 3D space. This information is now accessible to scientists because of the advent of super-resolution microscopy, especially single-molecule imaging (SMLM) which allows the positions of biomolecules to be mapped with nanometer precision. Well-curated, publically-accessible databases have been transformative across biology. Well established databases, such as GenBank are ubiquitously used, but the data they contain are relatively simplistic. Somewhat more complex data sets include protein structure databases, for example PDB and the associated predicted structures from DeepMind's AlphaFold2. This application builds on initial Alan Turing Institute, EPSRC and BBSRC investment with the aim to become a national and global resource for the storage, sharing, curating and processing of SMLM data. Once established, we will lay the foundations for a new field of -omics, nano-omics: the study of protein nanoscale organisation.Ultimatly, the resource will be a database where users can store, share and disseminate their SMLM data, benefitting the public engagement with science, aiding collaboration and helping meet data sharing mandates of funders and publishers. A community management structure will ensure the database follows best practice for research ethics and scientific excellence. The database will also feature advanced data analysis tools running in the cloud allwoing users to extract biologically relecvant information from uploaded datasets. This aids in bringing advanced statistical analysis to those without means and helping to democratize advanced imaging. Finally, we will conduct primary research into the meta-analysis of the uploaded data and initiate a new field of nano-omics - the study of the diversity of protein nanoscale organisation between proteins, cells and organisms.
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会议论文
Bayesian and machine-learning-based analysis of high-volume super-resolution microscopy data for molecular-level cell phenotyping
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批准号:BB/R007365/1
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项目类别:Research Grant
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资助金额:$50.84万
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财政年份:2018
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负责人:Dylan Owen
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依托单位:
海外基金