The Future of Omics Data Mining for Research and Drug Discovery
Q-omics is an innovative research platform empowering scientists to explore and analyze multi-omics data independently, freeing them from reliance on external services.
Pan-cancer consensus analysis of cross-associated multimodal omics data and the functional interpretation via NetCrafter, enable the identification of crucial cancer-related biomarkers, drug targets, and therapeutic strategies.
Wide range of applications include:
Undruggable targets and new modalities in drug discovery |
Analysis of functions, underlying mechanisms and disease phenotypes |
Consensus analysis for target and biomarker discovery |
Analysis of synthetic lethal gene pairs |
Tumor-specific surface proteins and neoantiens |
Biomarkers in immunotherapy response |
Tumor-infiltarting cells |
Pan-cancer agnostic target discovery |
Visualization and flexible analysis of user data |
And many more ... |
Available data integrated for cross-association analysis
| Data set | # Lineages | # Samples | # Entities | Data type | Source/Download | |
|---|---|---|---|---|---|---|
| Cell line data | RNA expression | 20 | 1,061 | 19,137 | RNA sequencing | CCLE[1]/Depmap Expression 20Q1[2] |
| Protein expression and phosphorylation | 20 | 899 | 214 | Reverse Phase Protein Array | Depmap/Protein Array[2] | |
| Protein abundance | 20 | 949 | 8,424 | Mass spectrometry | [11] | |
| sgRNA | 20 | 741 | 18,110 | CRISPR | Depmap/CRISPR 20Q1[2] | |
| shRNA | 20 | 587 | 16,800 | RNAi shRNA | Depmap/RNAi (Achilles + DRIVE + Marcotte, DEMETER2)[2] | |
| Drug response | 20 | 1,001 | 561 | Drug response | GDSC[3]/Depmap Sanger GDSC1 and GDSC2[2] | |
| Mutations | 20 | 1,281 | 18,731 | Exome sequencing | CCLE/Depmap Mutation 20Q1[2] | |
| Drug-induced RNA expression | 13 | 60 | 12,305 (genes) 15 (drugs) |
DNA microarray | NCI60[4]/GSE116436[5] | |
| RNA expression enriched GO function | 20 | 1,061 | 7,172 (GO) | Enrichment score | GO[14] | |
| Protein abundance enriched GO function | 20 | 848 | 7,172 (GO) | Enrichment score | GO[14] | |
| Tissue data | Tumor RNA expression | 34 | 9,951 | 38,311 | RNA sequencing | TCGA[6], GENIE[7], CPTAC[12] /GDC portal[8] |
| Protein expression and phosphorylation | 32 | 6,985 | 275 | Reverse Phase Protein Array | TCGA/GDC portal | |
| Protein abundance and post-translational modification | 10 | 1,067 | >50,000 | Mass spectrometry | CPTAC/PDC portal[13] | |
| Paired normal vs. tumor: RNA expression | 18 | 679 | 38,311 | RNA sequencing | TCGA/GDC portal | |
| Somatic mutations | 34 | 9,100 | 20,850 | Exome sequencing | TCGA, GENIE/GDC portal | |
| Infiltrating cells | 33 | 8,954 | 64 (cell types) | Cell type enrichment score | xCell[9]/xCell portal[10] | |
| RNA expression enriched GO function | 34 | 9,951 | 7,172 (GO) | Enrichment score | GO[14] | |
|
[1] Barretina et al., (2012) The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603-607. [2] https://depmap.org/portal/download/ [3] Yang, et al., (2013) Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Research. 41 (Database issue): D955 - D961. [4] Monks, et al. (2018) The NCI Transcriptional Pharmacodynamics Workbench: A Tool to Examine Dynamic Expression Profiling of Therapeutic Response in the NCI-60 Cell Line Panel. Cancer Res 78, 6807-6817. [5] https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE116436 [6] Cancer Genome Atlas Research, N., et al. (2013) The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet 45, 1113-1120. [7] AACR Project GENIE Consortium, (2017) AACR Project GENIE: Powering Precision Medicine through an International Consortium. Cancer Discovery 7(8):818-831. [8] https://portal.gdc.cancer.gov/ [9] Dvir A., Zicheng H. and Atul J.B. (2017) xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biology 18:220. [10] https://xcell.ucsf.edu/ [11] Gonçalves, et al. (2022) Pan-cancer proteomic map of 949 human cell lines. Cancer Cell 40(8), 835-849. [12] https://proteomics.cancer.gov/ [13] https://proteomic.datacommons.cancer.gov/pdc/ [14] https://geneontology.org/ |
||||||
Pre-calculated associated data pairs in Smart DBs
| Database name | Analysis type | # Tables | # Records |
|---|---|---|---|
| Cross association in cell line samples | Cross association hits between pairs of entities among 8 datasets | 31[1] | 1,817,144,623 |
| Cross association in patient samples | Cross association hits between pairs of entities among 6 datasets | 14[2] | 9,542,951,055 |
| Survival analysis in patient samples | Survival hits using 6 datasets | 6[3] | 1,002,083 |
| Survival analysis between omics datasets in patient samples | Survival hits between omics datasets using 6 datasets | 22[4] | 16,421,400,938 |
| Cross association between omics dataset and drug response in patient samples | Cross association hits between 5 datasets and drug response | 4[5] | 915,619 |
| Drug-induced RNA expression and GO function in cell line samples | Differentially expressed RNA expression and enriched GO function hits using drug concentrations and drug treatment times | 2[6] | 372,905 |
| Comparison between normal vs. tumor tissue in patient samples | Differential hits using 5 datasets | 5[7] | 662,331 |
| Synthetic lethal analysis in cell line and patient samples | Down-regulated gene pair hits between sgRNA vs. RNA expression and shRNA vs. RNA expression (cell line samples), and down-regulated survival hits between RNA expression vs. RNA expression (patient samples) | 3[8] | 12,406,678 |
| Gene, Drug, GO function summary | Hit summary per gene/drug/GO function in smart DB | 3[9] | 964,670 |
|
Abbreviation: Protein (RPPA) for protein expression and phosphorylation by Reverse Phase Protein Array (RPPA), Protein (MS) for Protein abundance and post-translational modification by Mass spectrometry [1] Drug response vs. Drug response, Drug response vs. Protein (RPPA), GO function enriched by Protein (MS) vs. Drug response, GO function enriched by RNA expression vs. Drug response, GO function enriched by RNA expression vs. Protein (MS), Mutation vs. Drug response, Mutation vs. Mutation, Mutation vs. RNA expression, Mutation vs. sgRNA, Mutation vs. shRNA, Protein (RPPA) vs. Mutation, Protein (RPPA) vs. Protein (RPPA), Protein (RPPA) vs. RNA expression, Protein (RPPA) vs. sgRNA, Protein (RPPA) vs. shRNA, Protein (MS) vs. Drug response, Protein (MS) vs. Mutation, Protein (MS) vs. Protein (RPPA), Protein (MS) vs. Protein (MS), Protein (MS) vs. RNA expression, Protein (MS) vs. sgRNA, Protein (MS) vs. shRNA, RNA expression vs. Drug response, RNA expression vs. RNA expression, sgRNA vs. Drug response, sgRNA vs. RNA expression, sgRNA vs. sgRNA, shRNA vs. Drug response, shRNA vs. RNA expression, shRNA vs. sgRNA, shRNA vs. shRNA [2] Infiltrating cell vs. Infiltrating cell, Infiltrating cell vs. Protein (MS), RNA expression vs. Infiltrating cell, RNA expression vs. RNA expression, RNA expression vs. Mutation, GSGene vs. Infiltrating cell, Protein (MS) vs. RNA expression, Protein (MS) vs. Protein (MS), Protein (MS) vs. Mutation, Mutation vs. Infiltrating cell, Protein (RPPA) vs. Infiltrating cell, Protein (RPPA) vs. RNA expression, Protein (RPPA) vs. Mutation, Protein (RPPA) vs. Protein (RPPA) [3] RNA expression, Mutation, Infiltrating cell, Protein, MS, GSGene [4] Infiltrating cell vs. Infiltrating cell, Infiltrating cell vs. Mutation, Infiltrating cell vs. Protein (MS), Infiltrating cell vs. Protein (RPPA), Infiltrating cell vs. RNA expression, RNA expression vs. Infiltrating cell, RNA expression vs. Mutation, RNA expression vs. Protein (MS), RNA expression vs. Protein (RPPA), RNA expression vs. RNA expression, Protein (MS) vs. Infiltrating cell, Protein (MS) vs. RNA expression, Protein (MS) vs. Protein (MS), Protein (MS) vs. Mutation, Mutation vs. Infiltrating cell, Mutation vs. Mutation, Mutation vs. Protein (MS), Mutation vs. Protein (RPPA), Mutation vs. RNA expression, Protein (RPPA) vs. Infiltrating cell, Protein (RPPA) vs. RNA expression, Protein (RPPA) vs. Mutation, Protein (RPPA) vs. Protein (RPPA) [6] Differentially expressed RNA expression and differentially enriched GO function [7] RNA expression, Protein (MS), Infiltrating cell, RNA expression enriched GO function, and Protein (MS) enriched GO function [8] sgRNA vs. RNA expression, shRNA vs. RNA expression, and RNA expression vs. RNA expression [9] Gene, drug, and GO function | |||