Q-omics

Q-omics: Integrated Multiomics Data Mining Platform for Cancer Research

Q-omics integrates multiomics and phenotypic datasets to enable data-driven identification of cancer biomarkers and therapeutic targets through customized analytical workflows.

Key Features:

• Multi-Database Integration: Integrates datasets from The Cancer Genome Atlas (TCGA), Genomics of Drug Sensitivity in Cancer (GDSC), Cancer Cell Line Encyclopedia (CCLE), National Cancer Institute (NCI), and DepMap, including mutations, gene expression profiles, patient survival data, immune scores, drug screening results, and RNA interference (RNAi) screening data.
• Algorithm-Guided Data Mining: Implements knowledge-based algorithms for optimized sample selection, filtering, and customized multiomics analysis.
• Statistical Visualization: Generates Kaplan–Meier survival curves, box plots, and scatter plots for quantitative interpretation of integrated datasets.
• High-Throughput Multiomics Processing: Applies specialized algorithms for scalable analysis and visualization of large-scale multiomics datasets.

Scientific Applications:

• Biomarker and Target Discovery: Identifies candidate biomarkers and therapeutic targets by analyzing integrated genomic, transcriptomic, phenotypic, drug response, and RNAi screening data in oncology.
• Drug Response and Survival Analysis: Evaluates associations between gene alterations, drug sensitivity profiles, and patient survival outcomes.

Methodology:

Q-omics aggregates heterogeneous datasets from TCGA, GDSC, CCLE, NCI, and DepMap, harmonizes multiomics and phenotypic variables, and applies knowledge-based algorithms to perform optimized sample selection, filtering, and statistical analyses, followed by visualization through Kaplan–Meier, box plot, and scatter plot generation.