Multi-Omics Cancer Analysis for Precision Medicine Insights
Explore predicted protein structures - try rotating and zooming!
💡 Try it yourself: Rotate by dragging, zoom with scroll wheel, and switch between examples to see both DNA mutation analysis and scRNA cell marker proteins!
Three simple steps from data to clinical insights
Submit your VCF files (DNA variants), scRNA-seq data (gene expression matrices), or DICOM medical images through our secure platform. No command-line expertise needed.
Advanced enrichment using KEGG and Reactome identifies functional convergence between DNA variants and RNA expression. Automated druggable target detection from 15+ databases including population genetics, protein structures, and clinical trials.
Get concise pathway-focused summaries with druggable targets, interactive 3D protein structures with mutation highlighting, therapeutic recommendations, clinical trial matching, and professional PDF reports.
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Powerful multi-omics analysis without the complexity
User-friendly web interface eliminates the need for command-line expertise or bioinformatics training. Upload data, run analysis, get results.
Explore predicted protein structures with mutation highlighting directly on the homepage. Rotate, zoom, and examine proteins like BRAF V600E, EGFR, KRAS, and scRNA markers (CD3D, MS4A1) in real-time 3D.
Tabbed 2D/3D charts (UMAP, t-SNE), sticky navigation with section jumps, dynamic summary cards, and comprehensive data organization across all analysis results.
Comprehensive enrichment from population genetics (71K+ genomes), protein structure predictions (200M+ structures), drug databases (1.6M+ compounds), protein interaction networks, pathway databases, cancer proteomics, and more.
Advanced interpretation powered by artificial intelligence for variant classification, cell type prediction, confidence scoring, and therapeutic matching with clinical evidence.
Advanced pathway enrichment using KEGG and Reactome identifies functional convergence between DNA variants and RNA expression. Detects druggable targets and therapeutic opportunities even when genes don't overlap.
Built on industry-leading tools and databases
Your data is encrypted and processed securely. We never share or sell your genomic data.
State-of-the-art deep learning algorithms for high-accuracy somatic mutation detection and variant annotation.
Get comprehensive results in minutes, not hours. Track progress with live status updates.
Download results as CSV/JSON data or professional PDF reports with protein structure visualizations, comprehensive analysis, and clinical interpretations.
Explore our curated collection of cancer genomics and single-cell RNA-seq datasets. Load any demo to instantly test Omics807's multi-omics analysis capabilities.
Real-world cancer VCF files with comprehensive variant enrichment from population genetics databases, protein structure predictions, drug databases, protein interaction networks, pathway annotations, and therapeutic matching. Each case includes Omics807 insights with role-specific interpretations.
Stage IIIA NSCLC with actionable EGFR L858R (exon 21) and TP53 R273H mutations. Demonstrates targeted therapy matching (Osimertinib) and clinical trial identification.
Stage IV melanoma with classic BRAF V600E hotspot mutation. Showcases comprehensive enrichment including enhanced 3D protein structure visualization with mutation highlighting, protein-protein interaction networks, and pathway analysis.
Stage III colorectal cancer with APC (Wnt pathway), KRAS G12D (RAS/MAPK), and TP53 mutations. Illustrates multi-pathway dysregulation and population frequency filtering.
HER2-positive breast cancer with ERBB2 amplification and PIK3CA H1047R hotspot. Highlights interactive protein structure predictions with wild-type vs mutant comparison, functional site highlighting, and comprehensive drug-target data.
Pancreatic ductal adenocarcinoma with KRAS G12D hotspot and TP53 mutations. Demonstrates pathway dysregulation and challenging therapeutic landscape.
Tumor microenvironment datasets (800 cells, 132 genes) with diverse immune landscapes. Each case includes cell type prediction, pathway enrichment, differential expression, trajectory inference, cell-cell interactions, and Omics807 biological insights.
High immune infiltration (32%) with CD8+ T cells, NK cells, M1/M2 macrophages. "Hot" tumor microenvironment with diverse immune response signatures.
M2-dominant (15%) with B cells, dendritic cells, rich fibroblast stroma. Immunosuppressive microenvironment with potential therapy resistance.
High tumor purity (50%), elevated Tregs (7%), M2-polarized microenvironment. Immunosuppressive landscape with regulatory T cell infiltration.
Highest CD8+ TILs (14%), mixed M1/M2, potential MSI-H signature. Immune-inflamed phenotype suggesting immunotherapy responsiveness.
"Cold" tumor: Low CD8+ (8%), highest M2 (18%) & fibroblasts (15%). Demonstrates immunosuppressive, stromal-rich microenvironment.
Pre-configured multi-sample batch analysis sets (2-5 samples) for comparative genomics and transcriptomics. Perfect for testing batch upload features and exploring concordance matrices, multi-patient comparisons, and cohort-level insights.
Three breast cancer samples with different molecular profiles: HER2-positive, EGFR-mutant, and BRAF V600E. Demonstrates multi-sample concordance analysis and unique variant discovery.
Four lung cancer samples covering key driver mutations: EGFR L858R, KRAS G12D (x2), and control. Ideal for mutation concordance and therapeutic landscape analysis.
Five colorectal cancer patients (maximum batch size) covering diverse CRC molecular subtypes: KRAS-mutant, APC-loss, MSS, TP53/APC, and BRAF V600E.
Compare immune landscapes across three cancer types: breast, lung, and melanoma tumor microenvironments. Reveals tissue-specific immune signatures and cell type distributions.
Four-sample immune cell comparison: PBMC control, colorectal TME, pancreatic TME, and lung TME. Highlights tissue-specific immune composition and infiltration patterns.
Five patient PBMC samples (maximum batch size) including healthy donor and cancer patients. Tests cell type consistency, patient variability, and batch analysis scalability.
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