Toolkit for Interpretation of Organelle Proteomics Data

细胞器蛋白质组数据解释工具包

• 批准号: BB/H024247/1
• 负责人: Kathryn Lilley
• 金额: $ 15.4万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FH024247%2F1
• 关键词: Toolkit; Interpretation; Organelle; Proteomics; Data

Organelle proteomics is an up and coming field as the functionality of proteins and cellular mechanisms are clearly linked to the subcellular locations of the proteins. Such is the growing prominence of the field of organelle proteomics, the Human Proteome Organisation (HUPO) is holding its annual Barbados Workshop in January 2010 on the subject in an attempt to move the field forward such that optimal protocols and data analysis are disseminated through the proteomics and cell biology communities. A major frustration within the field is that the collection of high quality data is very time consuming and expensive on resources. The data sets that are produced in general are extremely rich sources of information, however, to date these have not been analysed to their full potential because of lack of suitable statistical workflows. For example, a recent paper published in MCP (Andreyev et al, Mol, Cell Proteomics -on-line 2009) contains a very rich dataset which has only been superficially mined to produce a limited organelle marker data set, but the rest of the data is not fully analysed and languishes in an spreadsheet within the supplemental data. Basic analytical strategies, pioneered by the applicants' laboratories, have shown the potential of such datasets to produce robust organelle proteome lists containing organelle specific annotations of proteins of unknown localization. Even the applicants' substantial organelle proteomics datasets, however, have only been analysed to a limited extent with established statistical approaches. In this proposal we aim to create more sophisticated statistical tools building on what has already been established by the applicants, which will be enable assignment of proteins to subcellular location using semi-supervised pattern recognition algorithms. These will lead to assignment of protein-organelle membership, resolution of proteins association with multiple organelles, and identification changes in protein-organelle association across multiple experimental conditions. These tools will be produced as freely-available software for analysis of standard organelle proteomics data generated by utilisation of the most common approaches. Application of these novel statistical approaches will lead to the creation of optimal organelle proteomics datasets which will themselves be deposited in a proteomics data repository, PRIDE, which can be publically accessed. In summary, the proposal will create a much needed tool to allow robust analysis of organelle proteomics datasets, and enable re-analysis of existing very rich data sets such that the most optimal mining of these data is achieved. It will also offer optimal tools for analysis of future organelle proteomics datasets which are starting to be produced by the proteomics/cell biology communities in earnest. The above work plan will be expedited by a multidisciplinary team which includes, Kathryn Lilley, developer of the organelle proteomics technologies and Matthew Trotter a bioinfromatician and statistician.

细胞器蛋白质组学是一个新兴的领域，因为蛋白质的功能和细胞机制显然与蛋白质的亚细胞位置有关。由于细胞器蛋白质组学领域日益突出，人类蛋白质组组织正于2010年1月举行关于这一主题的年度巴巴多斯研讨会，试图推动该领域向前发展，以便通过蛋白质组学和细胞生物学社区传播最佳方案和数据分析。该领域内的一个主要问题是，高质量数据的收集非常耗时且资源昂贵。一般而言，所产生的数据集是极其丰富的信息来源，但由于缺乏适当的统计工作流程，迄今尚未充分分析这些数据集的潜力。例如，最近在《MCP》上发表的一篇论文(Andreyev等人，MOL，细胞蛋白质组学-在线2009)包含了一个非常丰富的数据集，它只被表面挖掘以产生有限的细胞器标记数据集，但其余的数据没有得到充分的分析，并在补充数据内的电子表格中萎靡不振。由申请人的实验室开创的基本分析策略表明，这种数据集具有产生强大的细胞器蛋白质组列表的潜力，该列表包含未知定位的蛋白质的细胞器特定注释。然而，即使是申请者的大量细胞器蛋白质组学数据集，也只在有限程度上用既定的统计方法进行了分析。在这项提案中，我们的目标是在申请人已经建立的基础上创建更复杂的统计工具，这将使使用半监督模式识别算法将蛋白质分配到亚细胞位置成为可能。这将导致蛋白质-细胞器成员的分配，蛋白质与多个细胞器的结合的解析，以及在多个实验条件下蛋白质-细胞器结合的鉴定变化。这些工具将作为免费提供的软件，用于分析利用最常见方法产生的标准细胞器蛋白质组学数据。这些新的统计方法的应用将导致最佳细胞器蛋白质组数据集的创建，这些数据集本身将被存放在蛋白质组学数据库PROID中，该数据库可供公众访问。总之，该提案将创建一个急需的工具，以便能够对细胞器蛋白质组学数据集进行强有力的分析，并能够重新分析现有的非常丰富的数据集，从而实现对这些数据的最优挖掘。它还将为未来细胞器蛋白质组学数据集的分析提供最佳工具，这些数据集正开始由蛋白质组学/细胞生物学社区认真产生。上述工作计划将由一个包括细胞器蛋白质组学技术开发商凯瑟琳·利利和生物信息学家兼统计学家马修·特罗特在内的多学科团队加快实施。

项目成果

Spatiotemporal proteomic profiling of the pro-inflammatory response to lipopolysaccharide in the THP-1 human leukaemia cell line.

• DOI: 10.1038/s41467-021-26000-9
• 发表时间: 2021-10-01
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Mulvey CM;Breckels LM;Crook OM;Sanders DJ;Ribeiro ALR;Geladaki A;Christoforou A;Britovšek NK;Hurrell T;Deery MJ;Gatto L;Smith AM;Lilley KS
• 通讯作者: Lilley KS

Identification of trans-golgi network proteins in Arabidopsis thaliana root tissue.

• DOI: 10.1021/pr4008464
• 发表时间: 2014-02-07
• 期刊: JOURNAL OF PROTEOME RESEARCH
• 影响因子: 4.4
• 作者: Groen, Arnoud J.;Sancho-Andres, Gloria;Breckels, Lisa M.;Gatto, Laurent;Aniento, Fernando;Lilley, Kathryn S.
• 通讯作者: Lilley, Kathryn S.

A draft map of the mouse pluripotent stem cell spatial proteome.

• DOI: 10.1038/ncomms9992
• 发表时间: 2016-01-12
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Christoforou A;Mulvey CM;Breckels LM;Geladaki A;Hurrell T;Hayward PC;Naake T;Gatto L;Viner R;Martinez Arias A;Lilley KS
• 通讯作者: Lilley KS

Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata.

• DOI: 10.1093/bioinformatics/btu013
• 发表时间: 2014-05-01
• 期刊: Bioinformatics (Oxford, England)
• 作者: Gatto L;Breckels LM;Wieczorek S;Burger T;Lilley KS
• 通讯作者: Lilley KS