Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Knowledge Management Center for Illuminating the Druggable Genome, Mount Sinai Center for Bioinformatics, Icahn 10 School of Medicine at Mount Sinai, New York, NY, USA.
Bioinformatics. 2019 Apr 1;35(7):1247-1248. doi: 10.1093/bioinformatics/bty763.
Mechanistic molecular studies in biomedical research often discover important genes that are aberrantly over- or under-expressed in disease. However, manipulating these genes in an attempt to improve the disease state is challenging. Herein, we reveal Drug Gene Budger (DGB), a web-based and mobile application developed to assist investigators in order to prioritize small molecules that are predicted to maximally influence the expression of their target gene of interest. With DGB, users can enter a gene symbol along with the wish to up-regulate or down-regulate its expression. The output of the application is a ranked list of small molecules that have been experimentally determined to produce the desired expression effect. The table includes log-transformed fold change, P-value and q-value for each small molecule, reporting the significance of differential expression as determined by the limma method. Relevant links are provided to further explore knowledge about the target gene, the small molecule and the source of evidence from which the relationship between the small molecule and the target gene was derived. The experimental data contained within DGB is compiled from signatures extracted from the LINCS L1000 dataset, the original Connectivity Map (CMap) dataset and the Gene Expression Omnibus (GEO). DGB also presents a specificity measure for a drug-gene connection based on the number of genes a drug modulates. DGB provides a useful preliminary technique for identifying small molecules that can target the expression of a single gene in human cells and tissues.
The application is freely available on the web at http://DGB.cloud and as a mobile phone application on iTunes https://itunes.apple.com/us/app/drug-gene-budger/id1243580241? mt=8 and Google Play https://play.google.com/store/apps/details? id=com.drgenebudger.
Supplementary data are available at Bioinformatics online.
在生物医学研究中，机制分子研究经常发现疾病中异常过表达或低表达的重要基因。然而，试图操纵这些基因以改善疾病状态具有挑战性。在此，我们揭示了 Drug Gene Budger（DGB），这是一个基于网络和移动应用程序，旨在帮助研究人员优先考虑预测对其感兴趣的靶基因表达有最大影响的小分子。使用 DGB，用户可以输入一个基因符号，并希望上调或下调其表达。应用程序的输出是一个经过排序的小分子列表，这些小分子已被实验确定可产生所需的表达效果。该表包括每个小分子的对数变换倍数、P 值和 q 值，报告 limma 方法确定的差异表达的显著性。提供了相关链接，以进一步探索有关靶基因、小分子和小分子与靶基因关系来源证据的知识。DGB 中包含的实验数据是从 LINCS L1000 数据集、原始 Connectivity Map（CMap）数据集和 Gene Expression Omnibus（GEO）中提取的特征编译而成。DGB 还基于药物调节的基因数量为药物-基因连接提供了特异性度量。DGB 为识别可靶向人类细胞和组织中单个基因表达的小分子提供了有用的初步技术。
该应用程序可在网络上免费获得，网址为 http://DGB.cloud，也可在 iTunes https://itunes.apple.com/us/app/drug-gene-budger/id1243580241?mt=8 和 Google Play https://play.google.com/store/apps/details?id=com.drgenebudger 上作为移动电话应用程序获得。
补充数据可在 Bioinformatics 在线获得。
