Microarray Data Standards and Supporting Applications

描述(由申请者提供)：我们上一轮的资助致力于创建基础数据标准和基础设施，以支持共享微阵列数据。MGED(微阵列和基因表达数据协会；www.mged.org)标准和基础设施的可用性以及我们使用和评估它们所积累的经验使我们处于一个非常有利的地位，可以直接向正在生成高通量基因表达数据的工作台生物学家提供工具和资源：我们现在提议将我们的主要努力从构建计算基础设施转向-这项新建议的总体目的是通过为工作台生物学家提供他们需要的工具来有效地共享基因表达数据并在他们的研究中利用注释良好的基因表达数据来促进科学发现。当这些标准和资源被生物医学研究人员共同使用时，我们创建和促进数据共享标准和资源的工作将产生最大的影响。 微阵列和其他高通量技术的标准的必要性和潜在益处是显而易见的，但迄今制定的标准的积极影响尚未完全实现。这在很大程度上是因为我们开发的标准和工具仍然需要专业知识，但目标用户是不是该领域的专家的工作台生物学家，也不应该期望他们成为专家。在这项申请中，除了建立下一代数据交换标准外，我们还提议使用我们在上一轮资助中建立的基础设施作为数据交换资源的基础，这些资源对实验室生物学家有用和可用。因此，我们的目标是： 1.开发工具，帮助研究人员轻松注释微阵列实验。 2.扩展流行的数据分析和可视化工具(BioConductor、MeV、GenePattern、Java TreeView)，使用MAGE-TAB编码的实验注释。 3.将MAGE-TAB数据交换标准推广到其他高通量生物医学数据，如超高通量测序。 4.提供生物学家友好的本体术语，这些术语可用于注释微阵列数据，也可用作计算分析中有意义的术语。 5.参与外联、教育和信息收集工作，使社区参与制定标准，并确保广泛使用和提出批评意见。

DESCRIPTION (provided by applicant): Our previous funding round of this project was devoted to creating the underlying data standards and infrastructure to support sharing microarray data. The availability of the MGED (Microarray and Gene Expression Data Society; www.mged.org) standards and infrastructure and our accumulated experience using and evaluating them puts us in an excellent position to deliver tools and resources directly to the bench biologists who are generating high-throughput gene expression data: We now propose to shift our main efforts away from building computational infrastructure - the over-arching purpose of this new proposal is to facilitate scientific discovery by providing bench biologists with the tools they need to effectively share gene expression data and to take advantage of well-annotated gene expression data in their research. Our work to create and promote data sharing standards and resources will have greatest impact when those standards and resources are in common use by biomedical researchers. The need for and potential benefits of standards for microarray and other high-throughput technologies is clear, yet the positive impact of the standards thus far developed has not yet been fully realized. This is in large part because the standards and tools we have developed still require expert knowledge, yet the targeted users are bench biologists who are not experts in this domain, and should not be expected to become so. In this application, in addition to building the next generation of data exchange standards, we are proposing to use the infrastructure we have built in the previous round of funding as the basis for data exchange resources that are useful and usable by bench biologists. Therefore, our aims are to: 1. Develop tools to help researchers easily annotate microarray experiments. 2. Extend popular data analysis and visualization tools (BioConductor, MeV, GenePattern, Java TreeView) to use MAGE-TAB-encoded experimental annotations. 3. Generalize the MAGE-TAB data exchange standard to work with other high-throughput biomedical data, such as ultra-high-throughput sequencing. 4. Provide biologist-friendly ontology terms that can be used to annotate microarray data as well as serve as meaningful terms in computational analyses. 5. Participate in outreach, education and information gathering efforts to engage the community in the development of standards and to ensure widespread use and critiques.