MindSpec, 8280 Greensboro Dr, McLean, VA 22102, USA.
BMC Med Genomics. 2011 Jan 27;4:15. doi: 10.1186/1755-8794-4-15.
In the post-genomic era, multi-faceted research on complex disorders such as autism has generated diverse types of molecular information related to its pathogenesis. The rapid accumulation of putative candidate genes/loci for Autism Spectrum Disorders (ASD) and ASD-related animal models poses a major challenge for systematic analysis of their content. We previously created the Autism Database (AutDB) to provide a publicly available web portal for ongoing collection, manual annotation, and visualization of genes linked to ASD. Here, we describe the design, development, and integration of a new module within AutDB for ongoing collection and comprehensive cataloguing of ASD-related animal models.
As with the original AutDB, all data is extracted from published, peer-reviewed scientific literature. Animal models are annotated with a new standardized vocabulary of phenotypic terms developed by our researchers which is designed to reflect the diverse clinical manifestations of ASD. The new Animal Model module is seamlessly integrated to AutDB for dissemination of diverse information related to ASD. Animal model entries within the new module are linked to corresponding candidate genes in the original "Human Gene" module of the resource, thereby allowing for cross-modal navigation between gene models and human gene studies. Although the current release of the Animal Model module is restricted to mouse models, it was designed with an expandable framework which can easily incorporate additional species and non-genetic etiological models of autism in the future.
Importantly, this modular ASD database provides a platform from which data mining, bioinformatics, and/or computational biology strategies may be adopted to develop predictive disease models that may offer further insights into the molecular underpinnings of this disorder. It also serves as a general model for disease-driven databases curating phenotypic characteristics of corresponding animal models.
在后基因组时代，对自闭症等复杂疾病的多方面研究产生了与发病机制相关的多种类型的分子信息。自闭症谱系障碍（ASD）的假定候选基因/基因座和 ASD 相关动物模型的快速积累，对其内容的系统分析提出了重大挑战。我们之前创建了自闭症数据库（AutDB），为正在进行的收集、手动注释和可视化与 ASD 相关的基因提供了一个公共可用的网络门户。在这里，我们描述了 AutDB 中一个新模块的设计、开发和集成，用于正在进行的 ASD 相关动物模型的收集和综合编目。
与原始 AutDB 一样，所有数据均从已发表的同行评议科学文献中提取。动物模型使用我们的研究人员开发的新的标准化表型术语词汇进行注释，旨在反映 ASD 的多种临床表现。新的动物模型模块与 AutDB 无缝集成，用于传播与 ASD 相关的各种信息。新模块中的动物模型条目与资源原始“人类基因”模块中的相应候选基因相关联，从而允许在基因模型和人类基因研究之间进行跨模式导航。尽管当前发布的动物模型模块仅限于小鼠模型，但它采用了可扩展的框架，可以轻松地在将来纳入其他物种和非遗传病因自闭症的模型。
重要的是，这个模块化的 ASD 数据库提供了一个平台，可以采用数据挖掘、生物信息学和/或计算生物学策略来开发预测疾病模型，从而进一步深入了解这种疾病的分子基础。它还为疾病驱动的数据库提供了一个通用模型，用于编目相应动物模型的表型特征。
