Toolkit for Interpretation of Organelle Proteomics Data

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。随着细胞器蛋白质组学领域的日益突出，人类蛋白质组学组织（HUPO）将于2010年1月就该主题举行巴巴多斯年度研讨会，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。该领域的一个主要挫折是收集高质量的数据非常耗时和昂贵的资源。总的来说，所产生的数据集是极为丰富的资料来源，但是，由于缺乏适当的统计工作流程，迄今尚未对这些数据集进行充分分析。例如，最近发表在MCP上的一篇论文（Andreyev等人，Mol，细胞蛋白质组学- 2009年在线）包含一个非常丰富的数据集，该数据集仅被肤浅地挖掘以产生有限的细胞器标记数据集，但其余数据没有得到充分分析，并且在补充数据中的电子表格中黯然消失。由申请人实验室首创的基本分析策略已经显示出这些数据集的潜力，可以产生包含未知定位蛋白质的细胞器特异性注释的强大细胞器蛋白质组列表。然而，即使是申请人的大量细胞器蛋白质组学数据集，也只是在有限的程度上用既定的统计方法进行了分析。在本提案中，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结合的分辨率，以及在多个实验条件下蛋白质-细胞器结合的识别变化。这些工具将作为免费软件生产，用于分析利用最常用方法生成的标准细胞器蛋白质组学数据。这些新统计方法的应用将导致最佳细胞器蛋白质组学数据集的创建，这些数据集本身将存储在蛋白质组学数据存储库PRIDE中，该存储库可以公开访问。总之，该提案将创建一个非常需要的工具，允许细胞器蛋白质组学数据集的鲁棒分析，并能够重新分析现有的非常丰富的数据集，从而实现这些数据的最优挖掘。它还将为蛋白质组学/细胞生物学社区开始认真生产的未来细胞器蛋白质组学数据集的分析提供最佳工具。上述工作计划将由一个多学科团队加速完成，该团队包括细胞器蛋白质组学技术的开发人员Kathryn Lilley和生物信息学家和统计学家Matthew Trotter。

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.