Institute of Cardiovascular and Medical Sciences, University of Glasgow, BHF Glasgow Cardiovascular Research Centre, 126 University Place, Glasgow, G12 8TA, UK.
Sci Rep. 2017 Jan 12;7:40367. doi: 10.1038/srep40367.
Complex human traits such as chronic kidney disease (CKD) are a major health and financial burden in modern societies. Currently, the description of the CKD onset and progression at the molecular level is still not fully understood. Meanwhile, the prolific use of high-throughput omic technologies in disease biomarker discovery studies yielded a vast amount of disjointed data that cannot be easily collated. Therefore, we aimed to develop a molecule-centric database featuring CKD-related experiments from available literature publications. We established the Chronic Kidney Disease database CKDdb, an integrated and clustered information resource that covers multi-omic studies (microRNAs, genomics, peptidomics, proteomics and metabolomics) of CKD and related disorders by performing literature data mining and manual curation. The CKDdb database contains differential expression data from 49395 molecule entries (redundant), of which 16885 are unique molecules (non-redundant) from 377 manually curated studies of 230 publications. This database was intentionally built to allow disease pathway analysis through a systems approach in order to yield biological meaning by integrating all existing information and therefore has the potential to unravel and gain an in-depth understanding of the key molecular events that modulate CKD pathogenesis.
复杂的人类特征，如慢性肾脏病（CKD），是现代社会的主要健康和经济负担。目前，对分子水平上 CKD 发病和进展的描述仍不完全清楚。同时，高通量组学技术在疾病生物标志物发现研究中的广泛应用产生了大量难以轻易整理的不相关数据。因此，我们旨在开发一个以分子为中心的数据库，该数据库收录了来自现有文献出版物的与 CKD 相关的实验。我们建立了慢性肾脏病数据库 CKDdb，这是一个集成和聚类的信息资源，涵盖了 CKD 和相关疾病的多组学研究（microRNAs、基因组学、肽组学、蛋白质组学和代谢组学），通过进行文献数据挖掘和人工注释。CKDdb 数据库包含了 49395 个分子条目（冗余）的差异表达数据，其中 16885 个是来自 230 篇文献中 377 项人工注释研究的独特分子（非冗余）。该数据库的构建旨在通过系统方法进行疾病途径分析，通过整合所有现有信息来产生生物学意义，因此有可能揭示和深入了解调节 CKD 发病机制的关键分子事件。
