Q-omics 3

Smart platform for literature & multi-omics data mining

Q-omics 3 lets researchers explore pan-cancer cell line and patient data without relying on external analysis services.

It combines consensus meta-analysis, OmixMind for literature intelligence, and NetCrafter for ontology-based gene networks to support target and biomarker discovery.

Pan-cancer cross-association across multi-omics and clinical outcomes
Literature-guided hypotheses via OmixMind and network interpretation via NetCrafter

Target & biomarker discovery

Resistance mechanism analysis

Immuno-therapy biomarkers

Tumor-specific antigens

Many more ..

Core engine

Q-omics Core

Pan-cancer consensus analysis across RNA, protein, mutations, drug response, CRISPR/shRNA, infiltrating cells, and function-level layers.

Cross-association across 10+ omics dimensions
Survival and normal vs. tumor comparisons
Smart DB with pre-computed hits

Literature AI

OmixMind

Literature engine with refined search, journal-aware ranking, and premium LLM summaries to guide Q-omics analyses.

Highlights key papers and trends
Summarizes core findings with references
Suggests Q-omics workflows from user questions

Ontology graphs

NetCrafter

Ontology-based network explorer for interpreting complex gene relationships and phenotypes.

GO/HPO and pathway-level network views
Function- and phenotype-centric exploration
Hypothesis generation from gene sets

What you can do with Q-omics 3

Literature intelligence, multi-omics meta-analysis, and ontology-guided networks support key workflows in oncology research and drug discovery.

Undruggable targets & new modalities

Explore indirect vulnerabilities, pathway dependencies, and synthetic lethal partners around difficult targets using CRISPR/shRNA and drug response.

Mechanisms & disease phenotypes

Link genes, pathways, and phenotypes using multi-layer omics data and NetCrafter networks to understand functional mechanisms.

Consensus targets & biomarkers

Integrate cell line models and patient cohorts to find robust, lineage-agnostic or lineage-specific targets and biomarkers.

Synthetic lethal pairs & antigens

Mine synthetic lethal pairs and tumor-specific/neoantigen candidates by combining CRISPR/shRNA, expression, mutations, and neoantigen profiles.

Data coverage for cross-association analysis

Q-omics integrates pan-cancer cell line and patient data, enabling cross-association across RNA, protein, mutations, drug response, CRISPR/shRNA, infiltrating cells, and function-level features.

Cell line cohorts

20+ lineages and >1,000 cell lines with RNA, protein (RPPA, MS), mutations, CRISPR, shRNA, and drug response.

DepMap, CCLE, GDSC, NCI60

Patient cohorts

30+ tumor types with RNA, proteomics, mutations, infiltrating cells, survival, and paired normal vs. tumor data.

TCGA, GENIE, CPTAC, xCell

Function-level layers

Gene interaction and enrichment profiles derived from RNA, protein, CRISPR, and shRNA in both cell lines and patient samples.

GO, HPO and related ontologies

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-based GO enrichment	20	1,061	7,172 (GO)	Enrichment score	GO[14]
	Protein abundance-based GO enrichment	20	848	7,172 (GO)	Enrichment score	GO[14]
	sgRNA-based GO enrichment	20	635	7,172 (GO)	Enrichment score	GO[14]
	shRNA-based GO enrichment	20	585	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-based GO enrichment	34	9,951	7,172 (GO)	Enrichment score	GO[14]
	Protein abundance-based GO enrichment	10	1,029	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 Network 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 Smart DBs for instant discovery

Q-omics Smart DBs store billions of pre-computed hits, so cross-association, survival, and synthetic lethal patterns can be retrieved in seconds instead of hours.

Billions of pre-computed associations across cell line and patient cohorts

Database name	Analysis type	# Tables	# Records
Cross association in cell line samples	Cross-association hits between pairs of entities among 11 datasets	48[1]	2,112,908,469
Cross association in patient samples	Cross-association hits between pairs of entities among 7 datasets	18[2]	5,070,903,952
Survival analysis in patient samples	Survival hits using 7 datasets	8[3]	1,054,407
Survival analysis between omics datasets	Survival hits between omics datasets using 5 datasets	23[4]	14,492,821,675
Cross association with drug response (patients)	Cross-association hits between 4 datasets and drug response	6[5]	973,619
Drug-induced RNA expression (cell lines)	Differentially expressed RNA and enriched GO function hits across drug doses and treatment times	2[6]	372,905
Normal vs. tumor comparison	Differential hits using 5 datasets	5[7]	662,358
Synthetic lethal analysis	Down-regulated gene pair hits between sgRNA vs. RNA, shRNA vs. RNA (cell lines) and survival-based RNA vs. RNA pairs (patients)	3[8]	12,406,678
Neoantigen analysis	Differential neoantigen burden and tumor-specificity of 898 cell-surface genes	2[9]	80,170
Gene, drug, GO function summary	Hit density summary per gene / drug / GO term in Smart DBs	3[10]	1,371,696
Abbreviation: Protein (RPPA) – protein expression and phosphorylation by Reverse Phase Protein Array; Protein (MS) – protein abundance and post-translational modification by mass spectrometry. [1] Cross-association pairs among Drug response, Protein (RPPA), RNA expression, GO enrichments, Protein (MS), sgRNA, shRNA, Mutation, etc. [2] Cross-association pairs among infiltrating cells, Protein (MS), RNA expression, Mutation, Protein (RPPA), and GO enrichment layers. [3] Survival hits using RNA expression, Mutation, Infiltrating cell, Protein (RPPA), Protein (MS), RNA expression-based GO enrichment. [4] Survival hits between infiltrating cells, mutations, Protein (MS), Protein (RPPA), and RNA expression. [5] Gene/Mutation/Infiltrating cells/Protein (RPPA) vs. 15 drug responses, Gene/Infiltrating cells vs. Pembrolizumab response. [6] Differentially expressed RNA and differentially enriched GO functions. [7] Differential hits in RNA expression, Protein (MS), Infiltrating cells, and GO-based enrichments. [8] sgRNA vs. RNA expression, shRNA vs. RNA expression, RNA expression vs. RNA expression. [9] Cross-association hits between neoantigens and sgRNA/shRNA, and neoantigen-related survival hits. [10] Summary statistics per gene, drug, and GO function.