Pipeline for interpretation and storage of organelle proteomics data

细胞器蛋白质组学是蛋白质研究领域中的一个新兴领域。蛋白质组是一组由细胞表达的蛋白质或在任何给定时间和环境下在生物流体中发现的蛋白质。在真菌、植物和动物等更复杂的生物的细胞中，许多蛋白质存在于被称为细胞器的特定亚细胞结构中，它们在那里发挥功能。由于两个原因，生物学家非常希望确定蛋白质的亚细胞位置。首先，它可以帮助阐明它们在细胞中的作用，因为蛋白质是根据它们的功能在空间上组织的，而位置是它们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一种方式从污染物中纯化出来，从而从任何给定的细胞器中得到准确的蛋白质目录。最近出现了几种涉及定量策略的高通量方法，这些方法克服了生产纯细胞器进行分析的需要。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各种分离技术产生的部分富集组分中的分布模式，并且有可能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。对于所有这些方法，两个数据分析阶段是必不可少的；第一个问题涉及定量数据的适当规范化和消除系统偏差；第二种方法是对信号进行稳健的多变量处理，并对结果的置信度进行统计评估，这需要将分布和富集模式与已知细胞器标记相匹配。完整的数据集包含有关实验设计、数据操作和将蛋白质分配到亚细胞位置的信息，对生物学家来说将具有巨大的价值。到目前为止，还没有一种易于访问的、精简的、便捷的软件套件，可以以标准化的方式进行数据分析和捕获数据和元数据，然后由整个社区轻松访问和解释。在这个提案中，三个已经成功和富有成效的合作和非常互补的专业领域的小组，Lilley（细胞器蛋白质组学）Huber（统计，软件），Hermjakob和Martens （PRIDE数据库），都在剑桥地区，旨在为不断增长的细胞器蛋白质组学社区提供一个简单的细胞器管道，不仅有助于数据分析，还有助于数据存储和提交给所有主要期刊的数据。它的输出将很容易地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。

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..