Pipeline for interpretation and storage of organelle proteomics data

细胞器蛋白质组数据解释和存储的管道

• 批准号: BB/G024618/1
• 负责人: Kathryn Lilley
• 金额: $ 15.13万
• 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%2FG024618%2F1
• 关键词: Pipeline; interpretation; storage; organelle; proteomics

Organelle proteomics is an emerging field within the area of the study of the proteins. The proteome is the set of proteins expressed by a cell or found in a biological fluid at any given time and circumstances. Within cells of more complex organisms such as fungi, plants and animals, many proteins are found in specific subcellular structures called organelles, where they carry out their function. Determining the sub-cellular location(s) of a protein is very desirable to biologists for two reasons. Firstly, it can help elucidate their role in the cell as proteins are spatially organised according to their function, and location is an important determinant of the specificity of their molecular interactions. Secondly, it refines our knowledge of cellular processes by pinpointing certain activities to specific organelles. Unfortunately, most organelles cannot be purified away from contaminants in such a way as to lead to an accurate catalogue of proteins from any given organelle. Recently several high throughput methods have emerged involving quantitative strategies, which have overcome the need to produce a pure organelle for analysis. Each of these methods relies on quantitative proteomics to characterize the distribution pattern of organelles amongst partially enriched fractions generated by various separation technologies and have the potential to discriminate between genuine organelle residents and contaminants without preparation of pure organelles. For all of these methods, two data analysis stages are essential; the first deals with appropriate normalisation of quantitative data and removal of system bias; the second involves robust multivariate processing of the signals and a statistical assessment of the confidence in the results, which is required to match distribution and enrichment patterns to those of known organelle markers. Fully curated data sets containing information about experimental design, data manipulation and assignment of proteins to subcellular locations would be of immense value to biologists. To date there is no easily accessible, streamlined, facile software suites which allow data analysis and capturing of data and meta data in a standardized way which can then be easily accessed and interpreted by the community at large. In this proposal three groups that already have successful and fruitful collaborations in place and extremely complementary areas of expertise, Lilley (organelle proteomics) Huber (Statistics, software), Hermjakob and Martens (PRIDE database), all within the Cambridge area, aim to produce a facile organelle pipeline used by the growing organelle proteomics community and will aid not only data analysis, but data storage and presentation of data for submission to all the major journals. Its output will be easily accessible by a wide variety of biologists and will facilitate data sharing amongst a growing cohort of scientists..

细胞器蛋白质组学是蛋白质研究领域的一个新兴领域。蛋白质组是在任何给定的时间和情况下由细胞表达或在生物流体中发现的蛋白质的集合。在真菌、植物和动物等更复杂的生物体的细胞内，许多蛋白质存在于称为细胞器的特定亚细胞结构中，在那里它们执行其功能。确定蛋白质的亚细胞位置是生物学家非常期望的，原因有两个。首先，它可以帮助阐明它们在细胞中的作用，因为蛋白质根据其功能在空间上组织，并且位置是其分子相互作用特异性的重要决定因素。其次，它通过精确定位特定细胞器的某些活动来完善我们对细胞过程的认识。不幸的是，大多数细胞器不能以这样的方式从污染物中纯化出来，从而导致任何给定细胞器的蛋白质的准确目录。最近出现了几种涉及定量策略的高通量方法，这些方法克服了产生用于分析的纯细胞器的需要。这些方法中的每一种都依赖于定量蛋白质组学来表征细胞器在由各种分离技术产生的部分富集级分中的分布模式，并且具有在不制备纯细胞器的情况下区分真正的细胞器居民和污染物的潜力。对于所有这些方法，两个数据分析阶段是必不可少的;第一个涉及定量数据的适当归一化和系统偏差的去除;第二个涉及信号的稳健的多变量处理和结果置信度的统计评估，这是将分布和富集模式与已知细胞器标记物相匹配所必需的。包含有关实验设计、数据处理和蛋白质亚细胞位置分配信息的完整数据集对生物学家具有巨大价值。到目前为止，还没有一套易于使用、精简、简易的软件，可以以标准化的方式进行数据分析和获取数据和Meta数据，然后由广大社区容易地访问和解释。Lilley在该提案中，三个小组已经建立了成功且富有成效的合作，并且在专业知识领域极其互补，（细胞器蛋白质组学）Huber（统计学，软件），Hermjakob和Martens（PRIDE数据库），都在剑桥地区，旨在产生一个由不断增长的细胞器蛋白质组学社区使用的简易细胞器管道，不仅有助于数据分析，而是数据存储和提交给所有主要期刊的数据的呈现。它的输出将很容易被各种各样的生物学家访问，并将促进越来越多的科学家之间的数据共享。