Department of Biological Sciences, Sookmyung Women's University, Seoul 04310, Korea.
These authors contributed equally to this work.
Mol Cells. 2021 Nov 30;44(11):843-850. doi: 10.14348/molcells.2021.0169.
The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computational expertise. For this purpose, we developed innovative software that enables user-driven analyses assisted by knowledge-based smart systems. Publicly available data on mutations, gene expression, patient survival, immune score, drug screening and RNAi screening were integrated from the TCGA, GDSC, CCLE, NCI, and DepMap databases. The optimal selection of samples and other filtering options were guided by the smart function of the software for data mining and visualization on Kaplan-Meier plots, box plots and scatter plots of publication quality. We implemented unique algorithms for both data mining and visualization, thus simplifying and accelerating user-driven discovery activities on large multiomics datasets. The present Q-omics software program (v0.95) is available at http://qomics.sookmyung.ac.kr.
随着旁系组学和表型数据的快速增长，基于数据的癌症靶点和生物标志物的快速发现研究成为可能。因此，有必要开发方便的工具，使普通肿瘤学家和癌症科学家无需计算专业知识就可以进行定制的数据挖掘。为此，我们开发了创新的软件，使用户能够在基于知识的智能系统的辅助下进行分析。从 TCGA、GDSC、CCLE、NCI 和 DepMap 数据库中整合了突变、基因表达、患者生存、免疫评分、药物筛选和 RNAi 筛选的公共数据。通过软件的智能功能，对 Kaplan-Meier 图、箱线图和散点图进行数据挖掘和可视化，对样本的最佳选择和其他过滤选项进行指导，生成具有出版质量的可视化结果。我们为数据挖掘和可视化都实现了独特的算法，从而简化并加速了用户对大型多组学数据集的驱动式发现活动。目前的 Q-omics 软件程序（v0.95）可在 http://qomics.sookmyung.ac.kr 上获得。
