predict_expression_impact
Analyze how genetic variants affect gene expression using RNA-seq and CAGE data. Supports eQTL analysis and expression-related variant studies.
Instructions
Focus on gene expression effects only.
Analyzes RNA-seq and CAGE predictions for expression changes.
Perfect for: eQTL analysis, expression-related variants.
Example: "Analyze expression impact of rs744373"
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| chromosome | Yes | ||
| position | Yes | ||
| ref | Yes | ||
| alt | Yes | ||
| tissue_type | No |
Implementation Reference
- scripts/alphagenome_bridge.py:371-387 (handler)Core handler function that executes the predict_expression_impact tool logic by setting specific output types for RNA-seq and CAGE, calling the base variant effect predictor, and returning expression-focused results.
def predict_expression_impact(client, params: Dict[str, Any]) -> Dict[str, Any]: """ Focus on gene expression effects only. """ params['output_types'] = [ dna_client.OutputType.RNA_SEQ, dna_client.OutputType.CAGE ] result = predict_variant_effect(client, params) return { 'variant': result['variant'], 'expression_predictions': result['predictions'].get('rna_seq', {}), 'fold_change': result['predictions'].get('rna_seq', {}).get('fold_change', 0), 'impact_level': result['interpretation']['impact_level'] } - src/tools.ts:312-332 (schema)Defines the tool schema including name, description, and input validation schema for variant parameters.
export const PREDICT_EXPRESSION_IMPACT_TOOL: Tool = { name: 'predict_expression_impact', description: `Focus on gene expression effects only. Analyzes RNA-seq and CAGE predictions for expression changes. Perfect for: eQTL analysis, expression-related variants. Example: "Analyze expression impact of rs744373"`, inputSchema: { type: 'object', properties: { chromosome: { type: 'string', pattern: '^chr([1-9]|1[0-9]|2[0-2]|X|Y)$' }, position: { type: 'number', minimum: 1 }, ref: { type: 'string', pattern: '^[ATGCatgc]+$' }, alt: { type: 'string', pattern: '^[ATGCatgc]+$' }, tissue_type: { type: 'string' }, }, required: ['chromosome', 'position', 'ref', 'alt'], }, }; - src/index.ts:176-182 (registration)MCP server tool call handler that routes 'predict_expression_impact' requests, validates input, calls the client bridge, and formats the response.
case 'predict_expression_impact': { const params = validateInput(variantPredictionSchema, args) as VariantPredictionParams; const result = await getClient().predictExpressionImpact(params); return { content: [{ type: 'text', text: JSON.stringify(result, null, 2) }], }; } - src/alphagenome-client.ts:298-311 (helper)TypeScript client method that bridges the tool call to the Python backend by invoking callPythonBridge with the 'predict_expression_impact' action.
async predictExpressionImpact(params: VariantPredictionParams): Promise<any> { try { return await this.callPythonBridge('predict_expression_impact', { chromosome: params.chromosome, position: params.position, ref: params.ref, alt: params.alt, tissue_type: params.tissue_type, }); } catch (error) { if (error instanceof ApiError) throw error; throw new ApiError(`Expression impact prediction failed: ${error}`, 500); } } - scripts/alphagenome_bridge.py:812-813 (registration)Backend Python dispatch logic in main() that registers and routes the 'predict_expression_impact' action to the handler function.
elif action == 'predict_expression_impact': result = predict_expression_impact(client, params)