Zhao Yue, Yang Chin-Rang, Raghuram Viswanathan, Parulekar Jaya, Knepper Mark A
Epithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland.
Epithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
Am J Physiol Renal Physiol. 2016 Oct 1;311(4):F787-F792. doi: 10.1152/ajprenal.00249.2016. Epub 2016 Jun 8.
Due to recent advances in high-throughput techniques, we and others have generated multiple proteomic and transcriptomic databases to describe and quantify gene expression, protein abundance, or cellular signaling on the scale of the whole genome/proteome in kidney cells. The existence of so much data from diverse sources raises the following question: "How can researchers find information efficiently for a given gene product over all of these data sets without searching each data set individually?" This is the type of problem that has motivated the "Big-Data" revolution in Data Science, which has driven progress in fields such as marketing. Here we present an online Big-Data tool called BIG (Biological Information Gatherer) that allows users to submit a single online query to obtain all relevant information from all indexed databases. BIG is accessible at http://big.nhlbi.nih.gov/.
由于高通量技术的最新进展，我们和其他研究人员已经生成了多个蛋白质组学和转录组学数据库，以在肾脏细胞的全基因组/蛋白质组规模上描述和量化基因表达、蛋白质丰度或细胞信号传导。来自不同来源的如此多数据的存在引发了以下问题：“研究人员如何在所有这些数据集中高效地找到给定基因产物的信息，而无需逐个搜索每个数据集？”这就是推动数据科学领域“大数据”革命的一类问题，这场革命推动了市场营销等领域的发展。在这里，我们展示了一个名为BIG（生物信息收集器）的在线大数据工具，它允许用户提交单个在线查询，以从所有索引数据库中获取所有相关信息。可通过http://big.nhlbi.nih.gov/访问BIG。
