CNRS, PAPPSO, UMR de, Génétique Végétale, Gif-sur-Yvette, France.
Proteomics. 2013 May;13(9):1457-66. doi: 10.1002/pmic.201200564. Epub 2013 Apr 5.
High throughput MS-based proteomic experiments generate large volumes of complex data and necessitate bioinformatics tools to facilitate their handling. Needs include means to archive data, to disseminate them to the scientific communities, and to organize and annotate them to facilitate their interpretation. We present here an evolution of PROTICdb, a database software that now handles MS data, including quantification. PROTICdb has been developed to be as independent as possible from tools used to produce the data. Biological samples and proteomics data are described using ontology terms. A Taverna workflow is embedded, thus permitting to automatically retrieve information related to identified proteins by querying external databases. Stored data can be displayed graphically and a "Query Builder" allows users to make sophisticated queries without knowledge on the underlying database structure. All resources can be accessed programmatically using a Java client API or RESTful web services, allowing the integration of PROTICdb in any portal. An example of application is presented, where proteins extracted from a maize leaf sample by four different methods were compared using a label-free shotgun method. Data are available at http://moulon.inra.fr/protic/public. PROTICdb thus provides means for data storage, enrichment, and dissemination of proteomics data.
高通量 MS 为基础的蛋白质组学实验产生大量复杂的数据,需要生物信息学工具来方便地处理它们。这些需求包括数据存档、向科学界传播数据、组织和注释数据以促进其解释的方法。我们在这里介绍 PROTICdb 的一个演变,这是一个数据库软件,现在可以处理 MS 数据,包括定量数据。PROTICdb 的开发尽可能独立于用于生成数据的工具。生物样本和蛋白质组学数据使用本体术语进行描述。嵌入了一个 Taverna 工作流,从而可以通过查询外部数据库自动检索与鉴定的蛋白质相关的信息。存储的数据可以以图形方式显示,并且“查询构建器”允许用户在不了解底层数据库结构的情况下进行复杂的查询。所有资源都可以使用 Java 客户端 API 或基于 REST 的 Web 服务进行编程访问,从而允许将 PROTICdb 集成到任何门户中。我们提供了一个应用示例,其中使用无标记shotgun 方法比较了从四个不同方法提取的玉米叶片样本中的蛋白质。数据可在 http://moulon.inra.fr/protic/public 上获得。因此,PROTICdb 为蛋白质组学数据的存储、丰富和传播提供了手段。