Analysis and Annotation Pipeline for Functional Genomics

说明： 这笔赠款建议为功能基因组数据开发一个可扩展、可扩展的自动化数据分析管道。功能基因组学，包括微阵列和蛋白质组学，正在迅速发展，数据集的规模迅速增加，每月都会出现新的分析方法。 由于没有解决典型实验问题的事实上的标准，多项分析的应用是可取的，但由于所需的努力，很少进行。此外，功能基因组数据的分析通常是一个多步骤的过程，每个步骤都使用许多可能的方法(例如，用于图像分析、数据归一化、统计分析、数据挖掘)，导致在使用多个分析时的组合工作爆炸。 本申请中提出的功能基因组数据流水线将提供自动执行多个分析的能力，将为添加新功能和数据类型提供容易的可扩展性，将提供一个分布式计算环境以提供足够的计算能力，并将集成自动注释以允许通过生物学知识来指导分析。该系统将利用企业Java Beans来提供健壮的服务器架构、用于动态生成Web界面的Java服务器页面以及用于优化软件架构的面向对象的设计模式。通过使用与Java反射机制耦合的Strategy设计模式，系统在运行期间将是可扩展的。功能基因组学数据集将被封装在数据对象中，其中包括到NCI caBIO对象的链接，以利用NCI生物信息学中心的数据资源。此外，还可以从网站和通过分布式注释系统检索注释。文档和测试将与开发并行进行，并将在设计和部署期间整合最终用户以调整用户界面。 最终的系统将大大提高研究人员充分探索他们不断增长的数据集并根据更大的生物学知识库解释他们的实验结果的能力。它将得到全力支持，并向社区开放源码。

DESCRIPTION: This grant proposes the development of an extendable, scalable automated data analysis pipeline for functional genomics data. Functional genomics, including microarrays and proteomics, is evolving quickly, with data sets increasing rapidly in size and new analysis methodologies appearing monthly. Because there are no de facto standards for addressing typical experimental questions, the application of multiple analyses is desirable, but rarely performed due to the effort required. Furthermore, the analysis of functional genomics data is generally a multi-step process, with many possible methods in use at each step (e.g., for image analysis, data normalization, statistical analysis, data mining), leading to a combinatorial explosion of effort when using multiple analyses. The functional genomics data pipeline proposed in this application will provide the ability to automatically perform multiple analyses, will provide easy extendibility for adding new functions and data types, will provide a distributed computing environment to provide adequate computational power, and will integrate automated annotation to allow analyses to be guided by biological knowledge. The system will utilize Enterprise Java Beans to provide a robust server architecture, Java server pages for dynamic generation of web interfaces, and object oriented design patterns to optimize the software architecture. The system will be extendable during operation through use of the Strategy design pattern coupled to the Java reflection mechanism. Functional genomics data sets will be encapsulated within data objects that include links to the NCI caBIO objects to utilize the NCI Center for Bioinformatics data resources. In addition, annotations will be retrievable from web sites and through the Distributed Annotation System. Documentation and testing will proceed in parallel with development, and will integrate end users during design and deployment to tune the user interface. The final system will provide dramatic improvements in researchers' abilities to fully explore their growing data sets and to interpret their experimental results in light of the larger biological knowledge bases. It will be fully supported and released to the community open source.