Electronic Access to Mouse Tumor Data

我们的总体目标是开发信息学基础设施，以支持使用小鼠作为人类癌症模型系统的基础癌症遗传学研究。 我们开发了小鼠肿瘤生物学数据库（MTB；http://tumor.informatics.jax.org）作为原型，以探索有效支持电子访问小鼠肿瘤数据的方法。 我们现在建议在此资源的基础上，将其从原型数据库成熟为强大的数据库系统，扩大数据覆盖的深度和广度，增强肿瘤图像的收集，并改进搜索能力和图形显示。 我们将整合肿瘤疾病的频率、发病率、遗传学和病理学方面的数据，重点是获取不同基因定义的小鼠品系（近交系、突变型和基因工程小鼠）中特征性发育的肿瘤的数据。为了实现我们的目标，我们将： 将 MTB 迁移到强大的关系数据库管理系统 开发新的用户界面、查询功能和图形显示 继续用有关肿瘤发生的菌株特异性模式的数据填充 MTB 扩大数据覆盖范围，以包括癌症易感性/严重程度修饰基因和 QTL 增强和扩展病理相关数据（文本和图像） 开发数据库结构以适应体内成像技术 在与兽医病理学家的合作基础上与人类癌症联盟的小鼠模型合作(MMHCC) 促进数据共享，增加了 MTB 与相关癌症相关资源之间的链接数量，特别是那些捕获基因表达谱的资源，这将进一步细化肿瘤亚型的分类。

Our overall goal is to develop an informatics infrastructure to support basic cancer genetics research using the mouse as a model system of human cancer. We have developed the Mouse Tumor Biology Database (MTB; http://tumor.informatics.jax.org) as a prototype to explore ways to effectively support electronic access to mouse tumor data. We now propose to build on this resource, maturing it from a prototype database into a robust database system, expanding the depth and breadth of data coverage, enhancing the collection of tumor images, and improving search capabilities and graphical displays. We will integrate data on frequency, incidence, genetics, and pathology of neoplastic disorders, emphasizing the acquisition of data on tumors that develop characteristically in different genetically defined strains of mice (inbred, mutant, and genetically engineered). To accomplish our goal we will: migrate MTB to a robust relational database management system develop new user interfaces, query capabilities, and graphical displays continue to populate MTB with data on strain-specific patterns of tumorigenesis expand data coverage to include cancer susceptibility/severity modifier genes and QTL enhance and expand pathology-related data (text and images) develop database structures to accommodate in-vivo imaging techniques build on collaborative efforts with veterinary pathologists work with the Mouse Models of Human Cancer Consortium (MMHCC) to facilitate data sharing increase the number of links between MTB and relevant, cancer- related resources, particularly those capturing gene expression profiling that will further refine the categorization of tumor subtypes.