Department of Ophthalmology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.
Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.
Nucleic Acids Res. 2018 Jan 4;46(D1):D886-D893. doi: 10.1093/nar/gkx770.
Autoantibodies are produced to target an individual's own antigens (e.g. proteins). They can trigger autoimmune responses and inflammation, and thus, cause many types of diseases. Many high-throughput autoantibody profiling projects have been reported for unbiased identification of serological autoantigen-based biomarkers. However, a lack of centralized data portal for these published assays has been a major obstacle to further data mining and cross-evaluate the quality of these datasets generated from different diseases. Here, we introduce a user-friendly database, AAgMarker 1.0, which collects many published raw datasets obtained from serum profiling assays on the proteome microarrays, and provides a toolbox for mining these data. The current version of AAgMarker 1.0 contains 854 serum samples, involving 136 092 proteins. A total of 7803 (4470 non-redundant) candidate autoantigen biomarkers were identified and collected for 12 diseases, such as Alzheimer's disease, Bechet's disease and Parkinson's disease. Seven statistical parameters are introduced to quantitatively assess these biomarkers. Users can retrieve, analyse and compare the datasets through basic search, advanced search and browse. These biomarkers are also downloadable by disease terms. The AAgMarker 1.0 is now freely accessible at http://bioinfo.wilmer.jhu.edu/AAgMarker/. We believe this database will be a valuable resource for the community of both biomedical and clinical research.
自身抗体是针对个体自身抗原(例如蛋白质)产生的。它们可以引发自身免疫反应和炎症,从而导致多种疾病。许多高通量自身抗体分析项目已经被报道用于无偏识别基于血清自身抗原的生物标志物。然而,这些已发表的检测方法缺乏集中的数据门户,这是进一步挖掘数据和交叉评估来自不同疾病的这些数据集质量的主要障碍。在这里,我们介绍了一个用户友好的数据库 AAgMarker 1.0,它收集了许多从蛋白质组微阵列上的血清分析中获得的已发表的原始数据集,并提供了一个挖掘这些数据的工具箱。AAgMarker 1.0 的当前版本包含 854 个血清样本,涉及 136092 种蛋白质。总共鉴定并收集了 7803 个(4470 个非冗余)候选自身抗原生物标志物,用于 12 种疾病,如阿尔茨海默病、贝切特病和帕金森病。引入了七个统计参数来定量评估这些生物标志物。用户可以通过基本搜索、高级搜索和浏览来检索、分析和比较数据集。这些生物标志物也可以按疾病术语下载。AAgMarker 1.0 现在可以在 http://bioinfo.wilmer.jhu.edu/AAgMarker/ 免费访问。我们相信这个数据库将成为生物医学和临床研究界的宝贵资源。