AlphaGenome MCP Server

Instructions

Implementation Reference

• src/tools.ts:9-72 (schema)

  Defines the MCP Tool schema for 'predict_variant_effect' including name, description, and detailed inputSchema for variant parameters.
  export const PREDICT_VARIANT_TOOL: Tool = { name: 'predict_variant_effect', description: `Predict the regulatory impact of a genetic variant using AlphaGenome AI. Powered by Google DeepMind's AlphaGenome model for accurate regulatory predictions. Analyzes how a single nucleotide change affects: - Gene expression (RNA-seq predictions) - Splicing patterns - Transcription factor binding - Chromatin accessibility - Histone modifications Perfect for: variant interpretation, GWAS follow-up, clinical genomics research. Example: "Analyze chr17:41234567A>T with AlphaGenome"`, inputSchema: { type: 'object', properties: { chromosome: { type: 'string', description: 'Chromosome (chr1-chr22, chrX, chrY)', pattern: '^chr([1-9]|1[0-9]|2[0-2]|X|Y)$', }, position: { type: 'number', description: 'Genomic position (1-based, positive integer)', minimum: 1, }, ref: { type: 'string', description: 'Reference allele (A, T, G, or C)', pattern: '^[ATGCatgc]+$', }, alt: { type: 'string', description: 'Alternate allele (A, T, G, or C)', pattern: '^[ATGCatgc]+$', }, output_types: { type: 'array', items: { type: 'string', enum: [ 'rna_seq', 'cage', 'splice', 'histone', 'tf_binding', 'dnase', 'atac', 'contact_map', ], }, description: 'Optional: specific analyses to run (default: all)', }, tissue_type: { type: 'string', description: 'Optional: tissue context (UBERON term, e.g., "UBERON:0001157" for brain)', }, }, required: ['chromosome', 'position', 'ref', 'alt'], }, };
• src/index.ts:111-130 (handler)

  MCP server CallTool handler specifically for 'predict_variant_effect': validates input, calls AlphaGenomeClient.predictVariant(), formats result with formatVariantResult, and returns formatted text content.
  case 'predict_variant_effect': { // Validate input const params = validateInput(variantPredictionSchema, args) as VariantPredictionParams; // Call AlphaGenome API const result = await getClient().predictVariant(params); // Format output const formatted = formatVariantResult(result); return { content: [ { type: 'text', text: formatted, }, ], }; }
• src/index.ts:99-101 (registration)

  Registers the listTools handler which returns ALL_TOOLS array including PREDICT_VARIANT_TOOL.
  server.setRequestHandler(ListToolsRequestSchema, async () => { return { tools: ALL_TOOLS }; });
• src/alphagenome-client.ts:167-185 (handler)

  AlphaGenomeClient.predictVariant method: bridges to Python subprocess with action 'predict_variant', maps TS params to Python format, handles errors.
  async predictVariant(params: VariantPredictionParams): Promise<VariantResult> { try { const result = await this.callPythonBridge<VariantResult>('predict_variant', { chromosome: params.chromosome, position: params.position, reference_bases: params.ref, alternate_bases: params.alt, output_types: params.output_types, tissue_type: params.tissue_type || 'brain', }); return result; } catch (error) { if (error instanceof ApiError) { throw error; } throw new ApiError(`Variant prediction failed: ${error}`, 500); } }
• scripts/alphagenome_bridge.py:111-253 (handler)

  Core implementation in Python bridge: calls AlphaGenome API predict_variant, processes outputs for multiple modalities (RNA-seq, splicing, TF binding), computes impact levels and returns structured result matching TS types.
  def predict_variant_effect(client, params: Dict[str, Any]) -> Dict[str, Any]: """ Predict the regulatory impact of a genetic variant. Args: client: AlphaGenome client instance params: Dictionary with chromosome, position, reference_bases, alternate_bases, etc. Returns: Dictionary with variant predictions matching TypeScript VariantResult type """ # Extract parameters chromosome = params.get('chromosome') position = params.get('position') ref_bases = params.get('reference_bases', params.get('ref')) alt_bases = params.get('alternate_bases', params.get('alt')) tissue_type = params.get('tissue_type', 'brain') output_types = params.get('output_types', ALL_MODALITIES) # Map tissue type to ontology term ontology_term = TISSUE_ONTOLOGY_MAP.get(tissue_type.lower(), "UBERON:0000955") # Create variant object variant = genome.Variant( chromosome=chromosome, position=position, reference_bases=ref_bases, alternate_bases=alt_bases ) # Create interval (resize to standard 1MB size like reference implementation) interval = variant.reference_interval.resize(dna_client.SEQUENCE_LENGTH_1MB) # Call AlphaGenome API outputs = client.predict_variant( interval=interval, variant=variant, ontology_terms=[ontology_term], requested_outputs=output_types if isinstance(output_types, list) else ALL_MODALITIES ) # Process predictions for each modality predictions = {} # RNA-seq effect if hasattr(outputs, 'alternate') and hasattr(outputs, 'reference'): if outputs.alternate.rna_seq and outputs.reference.rna_seq: rna_effect = safe_max_effect( outputs.alternate.rna_seq.values, outputs.reference.rna_seq.values, 'RNA_SEQ' ) rna_fc = calculate_fold_change( outputs.alternate.rna_seq.values, outputs.reference.rna_seq.values ) ref_mean = float(np.mean(outputs.reference.rna_seq.values)) alt_mean = float(np.mean(outputs.alternate.rna_seq.values)) predictions['rna_seq'] = { 'reference_score': ref_mean, 'alternate_score': alt_mean, 'fold_change': rna_fc, 'confidence': 0.85 # Placeholder - would need actual confidence from model } # Splice site analysis if outputs.alternate.splice_sites and outputs.reference.splice_sites: splice_effect = safe_max_effect( outputs.alternate.splice_sites.values, outputs.reference.splice_sites.values, 'SPLICE_SITES' ) ref_mean = float(np.mean(outputs.reference.splice_sites.values)) alt_mean = float(np.mean(outputs.alternate.splice_sites.values)) predictions['splice'] = { 'reference_score': ref_mean, 'alternate_score': alt_mean, 'delta': splice_effect, 'consequence': 'splice_site_disruption' if splice_effect > 0.2 else 'minimal_impact' } # Transcription factor binding tf_binding = [] if outputs.alternate.chip_tf and outputs.reference.chip_tf: tf_effect = safe_max_effect( outputs.alternate.chip_tf.values, outputs.reference.chip_tf.values, 'CHIP_TF' ) if tf_effect > 0.1: # Significant TF binding change ref_mean = float(np.mean(outputs.reference.chip_tf.values)) alt_mean = float(np.mean(outputs.alternate.chip_tf.values)) tf_binding.append({ 'factor': 'TF_Binding', # Would need metadata for specific TF names 'ref_score': ref_mean, 'alt_score': alt_mean, 'change': tf_effect }) if tf_binding: predictions['tf_binding'] = tf_binding # Determine impact level max_effect = max([ predictions.get('rna_seq', {}).get('fold_change', 0), predictions.get('splice', {}).get('delta', 0), max([tf.get('change', 0) for tf in predictions.get('tf_binding', [])], default=0) ], default=0) if abs(max_effect) > 0.5: impact_level = 'high' clinical_sig = 'likely_pathogenic' elif abs(max_effect) > 0.2: impact_level = 'moderate' clinical_sig = 'uncertain_significance' else: impact_level = 'low' clinical_sig = 'likely_benign' # Build interpretation interpretation = { 'impact_level': impact_level, 'clinical_significance': clinical_sig, 'recommendations': [ 'Further functional validation recommended' if impact_level == 'high' else 'Standard clinical follow-up', f'Tissue type: {tissue_type}', f'Ontology term: {ontology_term}' ] } # Return structured result matching TypeScript VariantResult interface return { 'variant': f"{chromosome}:{position}{ref_bases}>{alt_bases}", 'gene_context': None, # Would need gene annotation data 'predictions': predictions, 'interpretation': interpretation }