predict_chromatin_impact
Analyze how genetic variants affect chromatin accessibility using DNase and ATAC-seq data to predict chromatin state changes in regulatory regions.
Instructions
Focus on chromatin accessibility effects only.
Analyzes DNase and ATAC-seq predictions for chromatin state changes.
Perfect for: enhancer variants, regulatory region analysis.
Example: "Analyze chromatin impact of chr2:23456789C>T"
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:495-503 (handler)Core handler function for the predict_chromatin_impact tool. Specializes variant effect prediction for chromatin accessibility modalities (ATAC-seq and DNase-seq) by setting specific output_types and extracting relevant results.
def predict_chromatin_impact(client, params: Dict[str, Any]) -> Dict[str, Any]: """Focus on chromatin accessibility changes.""" params['output_types'] = [dna_client.OutputType.ATAC, dna_client.OutputType.DNASE] result = predict_variant_effect(client, params) return { 'variant': result['variant'], 'predictions': result['predictions'], 'impact_level': result['interpretation']['impact_level'] } - src/tools.ts:451-471 (schema)Tool definition including name, description, and input schema (JSON Schema) for parameter validation in the MCP tool 'predict_chromatin_impact'.
export const PREDICT_CHROMATIN_IMPACT_TOOL: Tool = { name: 'predict_chromatin_impact', description: `Focus on chromatin accessibility effects only. Analyzes DNase and ATAC-seq predictions for chromatin state changes. Perfect for: enhancer variants, regulatory region analysis. Example: "Analyze chromatin impact of chr2:23456789C>T"`, 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'], }, }; - scripts/alphagenome_bridge.py:822-823 (registration)Registration of the tool action in the Python bridge's main dispatcher, routing 'predict_chromatin_impact' calls to the handler function.
elif action == 'predict_chromatin_impact': result = predict_chromatin_impact(client, params) - src/index.ts:213-219 (registration)MCP server request handler case that dispatches 'predict_chromatin_impact' tool calls to the AlphaGenome client.
case 'predict_chromatin_impact': { const params = validateInput(variantPredictionSchema, args) as VariantPredictionParams; const result = await getClient().predictChromatinImpact(params); return { content: [{ type: 'text', text: JSON.stringify(result, null, 2) }], }; } - src/alphagenome-client.ts:364-370 (helper)TypeScript client method that bridges the MCP tool call to the Python bridge script by spawning the process and passing 'predict_chromatin_impact' action.
async predictChromatinImpact(params: VariantPredictionParams): Promise<any> { try { return await this.callPythonBridge('predict_chromatin_impact', params); } catch (error) { if (error instanceof ApiError) throw error; throw new ApiError(`Chromatin impact prediction failed: ${error}`, 500); }