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predict_tf_binding_impact

Analyze how genetic variants affect transcription factor binding sites using ChIP-seq predictions to identify regulatory changes.

Instructions

Focus on transcription factor binding effects only.

Analyzes TF binding site changes using ChIP-seq predictions.

Perfect for: TF binding site variants, regulatory element analysis.

Example: "Analyze TF binding impact of chr1:12345678G>A"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chromosomeYes
positionYes
refYes
altYes
tissue_typeNo

Implementation Reference

  • Tool schema definition including input validation schema for predict_tf_binding_impact
    export const PREDICT_TF_BINDING_IMPACT_TOOL: Tool = { name: 'predict_tf_binding_impact', description: `Focus on transcription factor binding effects only. Analyzes TF binding site changes using ChIP-seq predictions. Perfect for: TF binding site variants, regulatory element analysis. Example: "Analyze TF binding impact of chr1:12345678G>A"`, 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/tools.ts:709-730 (registration)
    Registration of predict_tf_binding_impact tool in the ALL_TOOLS array used by MCP server
    export const ALL_TOOLS: Tool[] = [ PREDICT_VARIANT_TOOL, BATCH_SCORE_TOOL, ASSESS_PATHOGENICITY_TOOL, PREDICT_TISSUE_SPECIFIC_TOOL, COMPARE_VARIANTS_TOOL, PREDICT_SPLICE_IMPACT_TOOL, PREDICT_EXPRESSION_IMPACT_TOOL, ANALYZE_GWAS_LOCUS_TOOL, COMPARE_ALLELES_TOOL, BATCH_TISSUE_COMPARISON_TOOL, PREDICT_TF_BINDING_IMPACT_TOOL, PREDICT_CHROMATIN_IMPACT_TOOL, COMPARE_PROTECTIVE_RISK_TOOL, BATCH_PATHOGENICITY_FILTER_TOOL, COMPARE_VARIANTS_SAME_GENE_TOOL, PREDICT_ALLELE_SPECIFIC_EFFECTS_TOOL, ANNOTATE_REGULATORY_CONTEXT_TOOL, BATCH_MODALITY_SCREEN_TOOL, GENERATE_VARIANT_REPORT_TOOL, EXPLAIN_VARIANT_IMPACT_TOOL, ];
  • MCP server dispatch handler for predict_tf_binding_impact tool call
    case 'predict_tf_binding_impact': { const params = validateInput(variantPredictionSchema, args) as VariantPredictionParams; const result = await getClient().predictTfBindingImpact(params); return { content: [{ type: 'text', text: JSON.stringify(result, null, 2) }], }; }
  • AlphaGenomeClient method that handles predict_tf_binding_impact by calling Python bridge
    async predictTfBindingImpact(params: VariantPredictionParams): Promise<any> { try { return await this.callPythonBridge('predict_tf_binding_impact', params); } catch (error) { if (error instanceof ApiError) throw error; throw new ApiError(`TF binding impact prediction failed: ${error}`, 500); }
  • Core Python handler implementing the predict_tf_binding_impact logic by specializing AlphaGenome predictions for TF binding
    def predict_tf_binding_impact(client, params: Dict[str, Any]) -> Dict[str, Any]: """Focus on TF binding effects only.""" params['output_types'] = [dna_client.OutputType.CHIP_TF] result = predict_variant_effect(client, params) return { 'variant': result['variant'], 'tf_binding': result['predictions'].get('tf_binding', []), 'impact_level': result['interpretation']['impact_level'] }

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