BioMCP

"""Comprehensive tests for AlphaGenome integration.""" from unittest.mock import MagicMock, patch import pandas as pd import pytest from biomcp.variants.alphagenome import ( _validate_inputs, predict_variant_effects, ) class TestInputValidation: """Test input validation for AlphaGenome.""" def test_valid_chromosomes(self): """Test validation accepts valid chromosome formats.""" valid_chroms = ["chr1", "chr22", "chrX", "chrY", "chrM", "chrMT"] for chrom in valid_chroms: # Should not raise _validate_inputs(chrom, 100, "A", "T") def test_invalid_chromosomes(self): """Test validation rejects invalid chromosome formats.""" invalid_chroms = ["1", "chr23", "chrZ", "chromosome1", "Chr1", ""] for chrom in invalid_chroms: with pytest.raises(ValueError, match="Invalid chromosome format"): _validate_inputs(chrom, 100, "A", "T") def test_invalid_position(self): """Test validation rejects invalid positions.""" with pytest.raises(ValueError, match="Position must be >= 1"): _validate_inputs("chr1", 0, "A", "T") with pytest.raises(ValueError, match="Position must be >= 1"): _validate_inputs("chr1", -10, "A", "T") def test_valid_nucleotides(self): """Test validation accepts valid nucleotides.""" valid_cases = [ ("A", "T"), ("C", "G"), ("ACGT", "TGCA"), ("a", "t"), ("acgt", "tgca"), # lowercase should work ] for ref, alt in valid_cases: # Should not raise _validate_inputs("chr1", 100, ref, alt) def test_invalid_nucleotides(self): """Test validation rejects invalid nucleotides.""" invalid_cases = [("N", "A"), ("A", "U"), ("AXG", "T"), ("A", "123")] for ref, alt in invalid_cases: with pytest.raises(ValueError, match="Invalid nucleotides"): _validate_inputs("chr1", 100, ref, alt) def test_empty_alleles(self): """Test validation rejects empty alleles.""" with pytest.raises( ValueError, match="Reference allele cannot be empty" ): _validate_inputs("chr1", 100, "", "A") with pytest.raises( ValueError, match="Alternate allele cannot be empty" ): _validate_inputs("chr1", 100, "A", "") class TestIntervalSizeCalculation: """Test interval size selection logic.""" @pytest.mark.asyncio async def test_interval_size_edge_cases(self): """Test interval size selection for edge cases.""" with patch.dict("os.environ", {}, clear=True): # Without API key, we should get early return result = await predict_variant_effects( chromosome="chr1", position=100, reference="A", alternate="T", interval_size=2000000, # Larger than max ) assert "AlphaGenome API key required" in result class TestCaching: """Test caching behavior.""" @pytest.mark.asyncio async def test_skip_cache_parameter(self): """Test that skip_cache parameter works.""" with patch.dict("os.environ", {}, clear=True): # First call result1 = await predict_variant_effects( chromosome="chr1", position=100, reference="A", alternate="T", skip_cache=True, ) # Second call with skip_cache result2 = await predict_variant_effects( chromosome="chr1", position=100, reference="A", alternate="T", skip_cache=True, ) # Both should show API key error assert "AlphaGenome API key required" in result1 assert "AlphaGenome API key required" in result2 class TestErrorHandling: """Test error handling and context.""" @pytest.mark.asyncio async def test_error_context_with_api_key(self): """Test that errors include proper context.""" with patch.dict("os.environ", {"ALPHAGENOME_API_KEY": "test-key"}): result = await predict_variant_effects( chromosome="chr1", position=100, reference="A", alternate="T", tissue_types=["UBERON:0002367"], skip_cache=True, ) # Should either get import error or API error with context if "AlphaGenome prediction failed" in result: assert "Context:" in result assert "chr1:100 A>T" in result assert "Tissue types:" in result @pytest.mark.asyncio async def test_input_validation_errors(self): """Test that input validation errors are raised.""" with patch.dict("os.environ", {"ALPHAGENOME_API_KEY": "test-key"}): # Invalid chromosome with pytest.raises(ValueError, match="Invalid chromosome format"): await predict_variant_effects( chromosome="invalid", position=100, reference="A", alternate="T", ) # Invalid nucleotides with pytest.raises(ValueError, match="Invalid nucleotides"): await predict_variant_effects( chromosome="chr1", position=100, reference="X", alternate="T", ) class TestThresholdParameter: """Test significance threshold parameter.""" @pytest.mark.asyncio async def test_custom_threshold(self): """Test that custom threshold is accepted.""" with patch.dict("os.environ", {}, clear=True): # Test with custom threshold result = await predict_variant_effects( chromosome="chr1", position=100, reference="A", alternate="T", significance_threshold=0.8, ) # Should work (get API key error, not parameter error) assert "AlphaGenome API key required" in result @pytest.mark.asyncio async def test_default_threshold(self): """Test that default threshold is used.""" with patch.dict("os.environ", {}, clear=True): # Test without threshold parameter result = await predict_variant_effects( chromosome="chr1", position=100, reference="A", alternate="T", ) # Should work with default assert "AlphaGenome API key required" in result class TestIntegration: """Integration tests with mocked AlphaGenome.""" @pytest.mark.asyncio async def test_successful_prediction_mock(self): """Test successful prediction with mocked AlphaGenome.""" with patch.dict("os.environ", {"ALPHAGENOME_API_KEY": "test-key"}): # Mock the AlphaGenome imports mock_genome = MagicMock() mock_dna_client = MagicMock() mock_variant_scorers = MagicMock() # Mock the model mock_model = MagicMock() mock_dna_client.create.return_value = mock_model # Mock scorers mock_variant_scorers.get_recommended_scorers.return_value = [ "scorer1" ] # Mock scores DataFrame mock_df = pd.DataFrame({ "output_type": ["RNA_SEQ"], "raw_score": [1.0], "gene_name": ["GENE1"], "track_name": ["tissue1"], }) mock_variant_scorers.tidy_scores.return_value = mock_df # Mock score_variant to return mock scores mock_model.score_variant.return_value = [MagicMock()] # Patch the imports with patch.dict( "sys.modules", { "alphagenome.data.genome": mock_genome, "alphagenome.models.dna_client": mock_dna_client, "alphagenome.models.variant_scorers": mock_variant_scorers, "alphagenome.data": MagicMock(genome=mock_genome), "alphagenome.models": MagicMock( dna_client=mock_dna_client, variant_scorers=mock_variant_scorers, ), }, ): result = await predict_variant_effects( chromosome="chr7", position=140753336, reference="A", alternate="T", interval_size=131072, skip_cache=True, ) # Check model was created with API key mock_dna_client.create.assert_called_once_with("test-key") # Check interval was created correctly mock_genome.Interval.assert_called_once() call_args = mock_genome.Interval.call_args assert ( call_args[1]["start"] == 140753336 - 65536 - 1 ) # 0-based assert call_args[1]["end"] == call_args[1]["start"] + 131072 # Check variant was created mock_genome.Variant.assert_called_once_with( chromosome="chr7", position=140753336, reference_bases="A", alternate_bases="T", ) # Check result contains expected formatting assert "AlphaGenome Variant Effect Predictions" in result assert "Gene Expression" in result assert "GENE1" in result