/biomarker-database-analysis

name: biomarker-database-analysis
description: Use when a researcher needs to query biomedical databases for biomarker discovery, build target profiles from UniProt/Open Targets/STRING, rank biomarker candidates by evidence strength, or generate SQL queries against clinical genomic databases.

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 identifier
• `survival_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: 1. Write queries as single lines (no newlines) 2. Never modify column names from the schema 3. Use aggregation (COUNT, AVG, GROUP BY) to reduce output size 4. Always validate with `refine_sql` before 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