Abstract 5035: Q-omics: smart software for assisting oncology and cancer research

Abstract Increased multi-level omics data has enabled data-driven studies on cancer drugs, targets and biomarkers. Thus, it is necessary to develop comprehensive tools for oncologists and cancer scientists to carry out extensive data mining without computational expertise. For this purpose, we have developed innovative software that enables user-driven analyses on cancer omics data, assisted by knowledge-based smart systems. Publicly available multi-level omics data of mutations, gene/protein expression, patient survival, immune score (tumor infiltrating cells), drug screening and RNAi (shRNA and CRISPR) screenings on patient samples and cell lines, were integrated from the TCGA, GDSC, CCLE, NCI and DepMap databases. Q-omics provides user-friendly interface for calculation and visualization of cross-associated data pairs retrieved from integrated datasets. The optimal selection of samples, datasets and/or other filtering options is guided by knowledge-bases of the software for the quick and easy finding of significant associations between data pairs. Furthermore, implemented smart algorithms prioritize significant hits based on consensus scoring methods. Consensus scoring using multiple statistical tests with varied sample (or lineage) selection, enriches noise-free, robust cross-associated pairs in the hit list. We believe that Q-omics provide simple but powerful tools for all areas of oncology and cancer research. The latest version of Q-omics software is available at http://qomics.sookmyung.ac.kr. Citation Format: Sukjoon Yoon, Euna Jeong, Sumin Jeong, Jieun Lee, Youngju Kim. Q-omics: smart software for assisting oncology and cancer research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5035.