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应用。将基因表达和蛋白质组学数据从实验室到临床的转化的一个困难是，我们目前无法将这些发现相互联系起来，也无法将其与临床测量结果联系起来。使用微阵列或蛋白质组学研究特定生物过程的转化研究人员将希望收集尽可能多的相关公开数据集，以比较结果。希望将临床或化学数据与多个基因组或蛋白质组学测量相关联的翻译研究者将希望找到并加入相关数据集。不幸的是，查找和连接相关的数据集是特别具有挑战性的今天，因为这些数据的有用的注释仍然表示只有非结构化的自由文本，限制了它的二次使用。我们试图回答的一个问题是，在生物医学本体的先前投资是否可以提供杠杆作用，以自动化的方式确定基因组数据的背景，从而使基因表达和蛋白质组数据的整合和基因组数据的二次使用在多个研究领域超出了那些数据集最初的目标。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，统一医学语言系统（UMLS），由国家医学图书馆建立和支持，验证和传播映射，（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-背景映射以在微阵列和蛋白质组学储存库内和跨微阵列和蛋白质组学储存库发现和整合数据。为了使这些工具与生物医学研究人员保持相关，我们在乳腺癌，器官移植和T细胞生物学领域纳入了三个驱动生物学项目（DBP）。为了实现这些DBP，我们的工具和映射将用于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连接实验数据。要解决DBPs问题，我们将重点开发一套可扩展的工具，这些工具可以访问和分析涵盖各种疾病的实验数据。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，我们将确保我们的研究结果具有广泛的适用性，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.