Biomarker database analysis

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