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A curated, publically-accessible database of protein nanoscale organisation

A curated, publically-accessible database of protein nanoscale organisation
蛋白质纳米级组织的精选、可公开访问的数据库
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 --

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中文摘要
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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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Bayesian and machine-learning-based analysis of high-volume super-resolution microscopy data for molecular-level cell phenotyping
  • 批准号:
    BB/R007365/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $50.84万
  • 财政年份:
    2018
  • 负责人:
    Dylan Owen
  • 依托单位:
海外基金