Your First Query
Learn how to write effective queries and understand what BioQuery can answer.
What You Can Ask
BioQuery understands natural language questions about cancer genomics. Here are the main types of queries:
Gene Expression Comparisons
Compare expression levels between cancer types or conditions:
Is DDR1 expression higher in papillary RCC vs clear cell RCC?
Compare EGFR expression between lung adenocarcinoma and squamous cell carcinoma
How does MYC expression differ between ER+ and ER- breast cancer?Tumor vs Normal
Compare tumor samples to normal tissue:
Is TP53 upregulated in breast cancer compared to normal breast tissue?
What's the fold change of BRCA1 in ovarian tumors vs normal?
Is KRAS expression different in pancreatic cancer vs normal pancreas?Mutation Frequency
Calculate how often genes are mutated in specific cancers:
What percentage of glioblastoma patients have IDH1 mutations?
How common is BRAF V600E in melanoma?
What's the TP53 mutation rate in colorectal cancer?Survival Analysis
Examine how gene expression relates to patient outcomes:
Does high DDR1 expression predict worse survival in kidney cancer?
Is BRCA1 expression associated with overall survival in ovarian cancer?
Do patients with high MYC expression have worse prognosis in lymphoma?Writing Effective Queries
Be Specific About Cancer Types
Use specific cancer type names or TCGA abbreviations for best results.
Good examples:
- “papillary renal cell carcinoma” or “KIRP”
- “lung adenocarcinoma” or “LUAD”
- “glioblastoma multiforme” or “GBM”
Avoid vague terms:
- “kidney cancer” (which type?)
- “lung cancer” (adenocarcinoma or squamous?)
Use Standard Gene Symbols
Use official HGNC gene symbols:
| ✅ Use This | ❌ Not This |
|---|---|
| TP53 | p53 |
| EGFR | EGF receptor |
| BRCA1 | breast cancer gene |
Common TCGA Cancer Codes
| Code | Cancer Type |
|---|---|
| BRCA | Breast invasive carcinoma |
| LUAD | Lung adenocarcinoma |
| LUSC | Lung squamous cell carcinoma |
| GBM | Glioblastoma multiforme |
| KIRC | Kidney renal clear cell carcinoma |
| KIRP | Kidney renal papillary cell carcinoma |
| COAD | Colon adenocarcinoma |
| PRAD | Prostate adenocarcinoma |
| OV | Ovarian serous cystadenocarcinoma |
| SKCM | Skin cutaneous melanoma |
Example Walkthrough
Let’s run through a complete example:
1. Type Your Question
Is DDR1 expression higher in papillary RCC compared to clear cell RCC?2. BioQuery Processes Your Query
Behind the scenes, BioQuery:
- Parses your natural language question
- Identifies: gene (DDR1), cancer types (KIRP, KIRC), analysis type (differential expression)
- Generates and runs a SQL query against TCGA data
- Performs statistical analysis
- Creates visualizations
3. Review Your Results
You’ll receive a Query Card with:
- Answer: “DDR1 expression is significantly higher in papillary RCC (KIRP) compared to clear cell RCC (KIRC)…”
- Figure: Interactive boxplot comparing expression
- Statistics: p-value, fold change, sample sizes
- Methods: Ready-to-copy text for your paper
4. Use Your Results
- Export: Download PNG/SVG for presentations
- Share: Copy permalink for collaborators
- Cite: Use the methods text in your paper
Troubleshooting
”I don’t understand that question”
Try rephrasing with:
- Specific gene symbols (TP53, not p53)
- Specific cancer types (LUAD, not lung cancer)
- Clearer comparison structure
”No data available”
The gene or cancer type may not be in our database. Try:
- Checking the gene symbol spelling
- Using a different cancer type
- Verifying the gene is expressed in that tissue
Unexpected Results
If results seem wrong:
- Check that you’re comparing the right cancer types
- Verify the gene symbol is correct
- Look at the sample sizes - small N may be unreliable