aws-samples Biomarker Database Analysis
When to use this skill
- Researcher asks to find biomarkers associated with a disease or cancer type
- Query clinical genomic databases for survival, gene expression, or mutation data
- Build protein/target profiles from biomedical databases
- Rank biomarker candidates by statistical evidence (p-value, effect size)
- Generate or optimize SQL for biomarker data retrieval
MCP Servers Used
biomni-research— for external biomedical database queries (UniProt, Open Targets, STRING, ClinVar)- Clinical genomic databases may use separate tools (Redshift/Athena) depending on deployment
Workflow: Query Clinical Genomic Database
Step 1: Understand the schema before querying
Always retrieve the database schema first to understand available tables and columns.
Tool: get_schema
Purpose: Retrieve table names, column names, data types, and descriptions
Key columns in a typical clinical genomic table:
case_id-- patient identifiersurvival_status-- alive/dead (boolean or 0/1)survival_duration-- time in days or years- Gene expression columns (e.g.,
gdf15,lrig1,cdh2,postn,vcan) - Clinical metadata:
age_at_histological_diagnosis,smoking_status,chemotherapy,histology
Step 2: Formulate and refine the SQL query
Decision tree for query type:
- Patient demographics -> Simple SELECT with WHERE/GROUP BY
- Biomarker expression -> SELECT gene columns with clinical filters
- Survival correlation -> SELECT survival_status, survival_duration, biomarker columns
- Cohort comparison -> GROUP BY with aggregation (COUNT, AVG)
Rules:
- Write queries as single lines (no newlines)
- Never modify column names from the schema
- Use aggregation (COUNT, AVG, GROUP BY) to reduce output size
- Always validate with
refine_sqlbefore execution
Tool: refine_sql
Input: sql (the query), question (rationale for this step -- not the user's original question)
Purpose: Optimize for efficiency, add aggregation, fix column references
Step 3: Execute and interpret results
Tool: query_redshift (or query_database)
Input: The refined SQL query
Output: Row-level results from the clinical database
Step 4: Build target profiles from external databases
For deeper biomarker validation, use the biomni-research MCP server with natural language queries:
| Database | Query approach |
|---|---|
| UniProt | "CDK4 protein function, domains, post-translational modifications" |
| Open Targets | "CDK4 disease associations and genetic evidence scores" |
| STRING | "CDK4 protein-protein interaction network" |
| ClinVar | "CDK4 pathogenic variants and clinical significance" |
Step 5: Rank candidates by evidence strength
Scoring framework for biomarker prioritization:
| Evidence type | Weight | Source |
|---|---|---|
| Statistical significance (p < 0.05) | High | Cox regression from clinical data |
| Known pathogenic association | High | ClinVar, Open Targets |
| Protein interaction in disease network | Medium | STRING (confidence > 0.7) |
| Literature support (3+ publications) | Medium | PubMed |
| Gene expression differential | Medium | Clinical database |
| Functional annotation match | Low | UniProt |
Query Patterns
Find top biomarkers for survival:
SELECT survival_status, survival_duration, gdf15, lrig1, cdh2, postn, vcan FROM clinical_genomic WHERE chemotherapy = 'Yes'
Cohort demographics:
SELECT smoking_status, COUNT(DISTINCT case_id) AS num_patients FROM clinical_genomic WHERE age_at_histological_diagnosis > 50 GROUP BY smoking_status
Disease-specific expression:
SELECT survival_status, COUNT(*) AS count FROM clinical_genomic WHERE histology = 'Adenocarcinoma' GROUP BY survival_status
Key Conventions
- Map survival_status:
False/Alive = 0,True/Dead = 1 - Expression values are continuous (higher = more expressed in tumor)
- Always include quality filters and use parameterized queries when available
- Store query results in shared memory for downstream agents (statistician, pathway analyst)
- When results exceed 100 rows, summarize with aggregation before presenting to user