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: Royal Holloway University of London
• 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
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FK004131%2F1
• 关键词: GPU; based; performance; system; discovering

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.

具有完整基因组序列的生物体的名单不断增长，这导致了数千个功能仍然未知的基因的鉴定。这些基因可能潜在地参与重要的生物细胞功能，并且可能代表诊断和药物基因组学研究的重要靶标，并且具有工业和农学重要性。因此，生物学的一项主要任务是在基因组尺度上鉴定这些未表征基因的功能。生物信息学面临的挑战是开发算法，给定一个基因，可以预测其功能的假设。来自完整基因组的序列比较显示，基因复制，分歧和重排是在进化过程中驱动给定生物体的蛋白质组扩展的主要机制。这意味着蛋白质可以分为家族，其中成员可能执行类似的功能。因此，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的线索。蛋白质通常由几个结构域组成。结构域是蛋白质序列的一段，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。蛋白质的功能取决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。因此，我们希望有一个工具，可以根据蛋白质的结构将其分组为家族：所有具有相同结构的蛋白质应该属于同一组。开发这样一个工具正是本项目的目标。此外，我们在这里计划的工具也将能够为各种架构提出可能的功能角色。我们的工具旨在研究非常大的蛋白质组。只有利用图形处理单元（GPU）技术的最新进展，才能解决这种规模的问题。现代GPU对于图形非常高效，但其高度并行的结构使它们对于并行处理大块数据的算法非常有效-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蛋白质组的架构以及各种架构的可能功能角色。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Web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。

项目成果

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

• 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.

LanDis: the disease landscape explorer

• 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
• 作者: Caceres J

A network medicine approach to quantify distance between hereditary disease modules on the interactome.

• 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

Computational selection of transcriptomics experiments improves Guilt-by-Association analyses.

• DOI: 10.1371/journal.pone.0039681
• 发表时间: 2012
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Bhat P;Yang H;Bögre L;Devoto A;Paccanaro A
• 通讯作者: Paccanaro A