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http://dx.doi.org/10.14348/molcells.2021.0169

Q-omics: Smart Software for Assisting Oncology and Cancer Research  

Lee, Jieun (Department of Biological Sciences, Sookmyung Women's University)
Kim, Youngju (Department of Biological Sciences, Sookmyung Women's University)
Jin, Seonghee (Department of Biological Sciences, Sookmyung Women's University)
Yoo, Heeseung (Department of Biological Sciences, Sookmyung Women's University)
Jeong, Sumin (Department of Biological Sciences, Sookmyung Women's University)
Jeong, Euna (Research Institute of Women's Health, Sookmyung Women's University)
Yoon, Sukjoon (Department of Biological Sciences, Sookmyung Women's University)
Publication Information
Molecules and Cells / v.44, no.11, 2021 , pp. 843-850 More about this Journal
Abstract
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.
Keywords
biomarker; cancer bioinformatics; immune infiltrate; Kaplan-Meier plot; omics data mining; smart software;
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