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A GPU-based high performance system for discovering consensus domain architecture and functional annotation of protein families

A GPU-based high performance system for discovering consensus domain architecture and functional annotation of protein families
基于 GPU 的高性能系统,用于发现蛋白质家族的共识域架构和功能注释
批准号:
BB/K004131/1
负责人:
Alberto Paccanaro
金额:
$14.61万
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2012
资助国家:
英国
项目状态:
已结题
起止时间:
2012 至 --

项目摘要

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中文摘要
翻译
拥有完整基因组序列的生物名单不断增加,这导致了数以千计的功能尚不清楚的基因的鉴定。这些基因可能涉及重要的生物细胞功能,可能是诊断和药物基因组学研究的重要靶点,具有工业和农学意义。因此,生物学的一项主要任务是在基因组规模上确定这些未确定特征的基因的功能。生物信息学面临的挑战是开发算法,根据给定的基因,可以预测其功能的假说。对完整基因组的序列比较表明,基因复制、分歧和重排是在进化过程中驱动给定生物体蛋白质集扩张的主要机制。这意味着蛋白质可以分成不同的家族,在这些家族中,成员可能执行类似的功能。因此,对这些蛋白质家族的鉴定是至关重要的,因为它可以为蛋白质的功能提供重要线索。蛋白质通常由几个结构域组成。结构域是一段蛋白质序列,它可以独立于蛋白质链的其余部分进化。每个结构域形成一个紧凑的三维结构,它可以出现在各种不同的蛋白质中。蛋白质的功能依赖于不同结构域之间的相互作用以及它们之间的联系。换句话说,蛋白质的功能取决于蛋白质的结构域结构。因此,我们希望有一种工具可以根据蛋白质的结构将蛋白质分成不同的家族:所有具有相同结构的蛋白质都应该属于同一组。开发这样的工具正是本项目的目标所在。此外,我们在这里计划的工具还将能够为各种架构提供可能的功能角色。我们的工具旨在处理非常大的蛋白质集。只有利用图形处理单元(GPU)技术的最新进展,才能实现这种规模的问题的计算量。现代GPU对于图形处理非常高效,但其高度并行的结构使其对于并行处理大数据块的算法非常有效-甚至比通用CPU更有效。GPU技术的使用将使我们能够创建一个Web应用程序,科学家将使用该应用程序来获得非常大的一组蛋白质的结构以及各种结构的可能功能角色。重要的是,我们将定期在可用的主要基因组上运行我们的系统,因此我们将能够通过我们的网络服务器架构和这些基因组中所有蛋白质的相关注释。所有这些网络服务都将免费提供给科学界。
英文摘要
The list of organisms with completed genome sequence is continuously growing and this has led to the identification of thousands of genes whose function is still unknown. These genes could potentially be involved in important biological cell functions and could represent important targets for diagnostic and pharmacogenomics studies and be of industrial and agronomical importance. A major undertaking for biology is therefore that of identifying the function of these uncharacterized genes on a genomic scale. The challenge for bioinformatics is then to develop algorithms that, given a gene, can predict a hypothesis for its function.Comparisons of sequences from complete genomes have revealed that gene duplication, divergence and rearrangement are predominant mechanisms that drive the expansion of the set of proteins of a given organism during evolution. This means that proteins can be grouped into families, where members are likely to perform similar functions. The identification of these protein families is therefore central as it can provide important clues for the function of proteins.Proteins are often composed of several domains. A domain is segment of protein sequence that can evolve independently of the rest of the protein chain. Each domain forms a compact three-dimensional structure and it can appear in a variety of different proteins. Protein function depends on the mutual interplay between the distinct domains and the links between them. In other words, protein function depends on the domain architecture of the protein.Therefore we would like to have a tool that can group proteins into families according to their architecture: all proteins with the same architecture should belong to the same group. The development of such a tool is exactly the goal of this project. Moreover the tool that we plan here will also be able to suggest possible functional roles for the various architectures.Our tool is aimed at working on very large sets of proteins. The amount of calculations for problems of this size is only feasible by taking advantage of the latest advances in graphical processing unit (GPU) technology. Modern GPUs are very efficient for graphics but their highly parallel structure makes them extremely effective for algorithms where processing of large blocks of data is done in parallel - even more effective than general-purpose CPUs. The use of GPU technology will allow us to create a web application that will be used by scientists to obtain the architectures for very large set of proteins together with possible functional roles for the various architectures. Importantly, we shall periodically run our system on the major genomes available and we will thus be able to through our web server architectures and relative annotation for all the proteins in those genomes. All these web services will be made freely available to the scientific community.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1016/j.jbi.2023.104295
发表时间: 2023-03
期刊: JOURNAL OF BIOMEDICAL INFORMATICS
影响因子: 4.5
作者: [Casiraghi, Elena, Wong, Rachel, Hall, Margaret, Coleman, Ben, Notaro, Marco, Evans, Michael D., Tronieri, Jena S., Blau, Hannah, Laraway, Bryan, Callahan, Tiffany J., Chan, Lauren E., Bramante, Carolyn T., Buse, John B., Moffitt, Richard A., Sturmer, Til, Johnson, Steven G., Shao, Yu Raymond, Reese, Justin, Robinson, Peter N., Paccanaro, Alberto, Valentini, Giorgio, Huling, Jared D., Wilkins, Kenneth J.]
通讯作者: Wilkins, Kenneth J.
DOI: 10.1038/s41431-023-01511-9
发表时间: 2024-01-10
期刊: EUROPEAN JOURNAL OF HUMAN GENETICS
影响因子: 5.2
作者: [Caniza,Horacio, Caceres,Juan J., Paccanaro,Alberto]
通讯作者: Paccanaro,Alberto
Combining interactomes from multiple organisms: A case study on human-mouse
结合多种生物体的相互作用组:人鼠案例研究
DOI: 10.1109/clei.2016.7833324
发表时间: 2016
期刊:
影响因子: --
作者: [Caceres J]
通讯作者: Caceres J
DOI: 10.1038/srep17658
发表时间: 2015-12-03
期刊: Scientific reports
影响因子: 4.6
作者: [Caniza H, Romero AE, Paccanaro A]
通讯作者: Paccanaro A
共 7 条
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    • 依托单位:
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