Integrating Microarray and Proteomic Data by Ontology-based Annotation

描述(由申请人提供)： 随着人类基因组计划的完成，有必要将基因组时代的发现转化为临床实用。利用基因表达和蛋白质组学数据进行床边翻译的一个困难是，我们目前无法将这些发现彼此联系起来，也无法与临床测量联系起来。使用微阵列或蛋白质组学研究特定生物过程的翻译研究人员将希望收集尽可能多的相关公开可用的数据集，以比较发现。想要将临床或化学数据与多个基因组或蛋白质组测量联系起来的翻译研究人员将希望找到并加入相关的数据集。不幸的是，如今查找和连接相关数据集尤其具有挑战性，因为这些数据的有用注释仍然仅由非结构化自由文本表示，这限制了它的二次使用。我们试图回答的一个问题是，生物医学本体的先前投资是否能够提供杠杆，以自动方式确定基因组数据的背景，从而实现基因表达和蛋白质组数据的集成，并在数据集最初针对的多个研究领域之外的多个研究领域二次使用基因组数据。解决这个问题的三个具体目标是(1)开发工具，将上下文注释全面映射到最大的生物医学本体，即由国家医学图书馆建立和支持的统一医学语言系统(UMLS)，验证并传播映射，(2)执行四管齐下的策略来评估实验-概念映射，以及(3)应用实验-上下文映射来在微阵列和蛋白质组库内和跨微阵列和蛋白质组库查找和集成数据。为了保持这些工具与生物医学研究人员的相关性，我们包括了三个驱动生物学项目(DBP)，分别涉及乳腺癌、器官移植和T细胞生物学领域。为了完成这些DBP，我们的工具和映射将被用于在微阵列和蛋白质组储存库内和跨微阵列和蛋白质组储存库查找和连接实验数据。让DBP解决问题将使我们的开发重点放在一组可扩展的工具上，这些工具可以访问和分析涵盖多种疾病的实验数据。通过我们由世界知名的NIH资助的研究人员组成的咨询委员会，我们将确保我们的发现将具有广泛的适用性，并对各种生物医学研究人员有用。

DESCRIPTION (provided by applicant): With the completion of the Human Genome Project, there is a need to translate genome-era discoveries into clinical utility. One difficulty in making bench-to-bedside translations with gene-expression and proteomic data is our current inability to relate these findings with each other and with clinical measurements. A translational researcher studying a particular biological process using microarrays or proteomics will want to gather as many relevant publicly-available data sets as possible, to compare findings. Translational investigators wanting to relate clinical or chemical data with multiple genomic or proteomic measurements will want to find and join related data sets. Unfortunately, finding and joining relevant data sets is particularly challenging today, as the useful annotations of this data are still represented only by unstructured free-text, limiting its secondary use. A question we have sought to answer is whether prior investments in biomedical ontologies can provide leverage in determining the context of genomic data in an automated manner, thereby enabling integration of gene expression and proteomic data and the secondary use of genomic data in multiple fields of research beyond those for which the data sets were originally targeted. The three specific aims to address this question are to (1) develop tools that comprehensively map contextual annotations to the largest biomedical ontology, the Unified Medical Language System (UMLS), built and supported by the National Library of Medicine, validate, and disseminate the mappings, (2) execute a four-pronged strategy to evaluate experiment-concept mappings, and (3) apply experiment-context mappings to find and integrate data within and across microarray and proteomics repositories. To keep these tools relevant to biomedical investigators, we have included three Driving Biological Projects (DBPs), in the domains of breast cancer, organ transplantation, and T-cell biology. To accomplish these DBPs, our tools and mappings will be used to find and join experimental data within and across microarray and proteomic repositories. Having DBPs to address will focus our development on a set of scalable tools that can access and analyze experimental data covering a large variety of diseases. Through our advisory committee of world-renowned NIH-funded investigators, we will ensure that our findings will have broad applicability and are useful to a wide variety of biomedical researchers.