diff --git a/Claude.md b/Claude.md
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+You are a coding review agent.  Your job is to:
+  - Analyse the code base for test coverage - unit tests and integration tests.
+  - Test and log performance
+  - Analyse code to report on potential causes of poor performance (what are the rate-limiting steps?)
+  - Report on any code which follows poor practises, especially where this could lead to bugs - e.g. in corner/edge cases that we should be testing in integration tests.
+
+Background material:
+
+UberGraph: https://github.com/INCATools/ubergraph
+
+CellCensus API: https://chanzuckerberg.github.io/cellxgene-census/
+
+CxG Schema: https://github.com/chanzuckerberg/single-cell-curation/tree/main/schema/5.2.0
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diff --git a/code_review_analysis.md b/code_review_analysis.md
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+# Code Review Analysis: cxg-query-enhancer
+
+## Test Coverage Analysis
+
+### ✅ Unit Tests - Good Coverage
+- **15 passing tests** covering core functionality
+- **Three test modules**: `test_enhance.py`, `test_ontology_extractor.py`, `test_sparql_client.py`
+- **Good mocking strategy**: Proper use of mocks for external dependencies (SPARQL queries, Census API)
+- **Edge case coverage**: Tests handle error conditions, unsupported categories, missing organisms
+
+### ❌ Integration Tests - Missing
+- **No real integration tests**: All tests use mocks, no actual API calls to Ubergraph or CellXGene Census
+- **Missing end-to-end validation**: `run_cxg_query_enhancer.py` exists but not integrated into test suite
+- **No performance benchmarks**: No tests measuring actual response times from external APIs
+
+## Performance Analysis
+
+### ❌ Critical Performance Issues Identified
+
+**Major Bottleneck: SPARQL Query Latency**
+- Live test timed out at 60 seconds
+- SPARQL queries to Ubergraph taking ~40+ seconds each
+- Multiple sequential queries compound delays (per ontology term)
+- No timeout configuration for SPARQL requests
+
+**Rate-Limiting Steps:**
+1. **SPARQL queries to Ubergraph** (`enhancer.py:130-139`) - Each term requires 2+ queries
+2. **CellXGene Census data loading** (`enhancer.py:42-46`) - Full dataset scan per category
+3. **Sequential processing** - No parallelization of independent queries
+
+### Performance Issues in Code
+
+1. **No request timeouts** (`enhancer.py:111-139`):
+```python
+# SPARQLClient has no timeout configuration
+self.sparql = SPARQLWrapper(self.endpoint)
+```
+
+2. **Inefficient Census filtering** (`enhancer.py:11-54`):
+```python
+# Loads entire Census columns into memory
+df = (obs_reader.read(column_names=[ontology_column_name])
+      .concat().to_pandas())
+```
+
+3. **No connection pooling or caching** beyond @lru_cache on census data
+
+## Code Quality & Bug Risk Analysis
+
+### ❌ Poor Practices & Bug Risks
+
+**High-Risk Areas:**
+
+1. **Regex-based parsing** (`enhancer.py:447-483`):
+   - Complex regex for query parsing prone to edge case failures
+   - No validation of malformed queries
+   - Potential injection vulnerabilities with unsanitized inputs
+
+2. **Exception handling** (`enhancer.py:51-53`):
+```python
+except Exception as e:
+    logging.error(f"Error accessing CellXGene Census: {e}")
+    return None
+```
+   - Overly broad exception catching masks specific error types
+   - Silent failures could lead to incorrect results
+
+3. **String manipulation bugs** (`enhancer.py:575-591`):
+```python
+# Dangerous regex replacement without validation
+query_filter = re.sub(
+    rf"{category}\s+in\s+\[.*?\]",
+    f"{category} in [{expanded_terms_str}]",
+    query_filter, flags=re.IGNORECASE
+)
+```
+   - Could malform queries if regex matches incorrectly
+   - No validation of final query syntax
+
+**Edge Cases Needing Integration Tests:**
+
+1. **Network failures** - No graceful degradation when Ubergraph/Census unavailable
+2. **Large result sets** - Memory issues with thousands of ontology terms
+3. **Malformed queries** - No input sanitization or validation
+4. **Mixed organism queries** - Development stage handling with wrong organisms
+5. **Empty results** - Behavior when no terms match census data
+
+### ✅ Good Practices Found
+
+1. **Proper logging** throughout codebase
+2. **LRU caching** for census data (`@lru_cache`)
+3. **Comprehensive parameter validation** for organisms/categories
+4. **Clear separation of concerns** (SPARQLClient, OntologyExtractor, enhance)
+
+## Repository Structure Assessment
+
+- **Clean organization**: Source in `src/`, tests in `tests/`, docs in `notebooks/`
+- **Modern Python packaging**: Uses Poetry for dependency management
+- **Good documentation**: Comprehensive README with examples
+- **Missing CI/CD**: No GitHub Actions for automated testing
+
+## Recommendations
+
+### Critical (Fix Immediately)
+1. **Add request timeouts** to SPARQL queries (30s max)
+2. **Implement query validation** to prevent malformed outputs
+3. **Add integration tests** with real API calls (marked as slow tests)
+4. **Add error boundaries** for network failures
+
+### Important (Next Sprint)
+1. **Optimize Census data loading** - column-specific queries vs full scans
+2. **Implement parallel SPARQL queries** for multiple terms
+3. **Add input sanitization** for query filters
+4. **Create performance benchmarks** as part of test suite
+
+### Nice-to-Have
+1. **Connection pooling** for SPARQL client
+2. **Query result caching** beyond Census data
+3. **Async/await support** for better performance
+4. **More granular logging levels**
+
+## Query Syntax Documentation Research
+
+### ✅ SOMA/TileDB Query Syntax Found
+
+**Official Documentation**: The `obs_value_filter` uses **SOMA value_filter syntax** which is well-documented:
+
+**Supported Syntax Pattern**:
+```
+value_filter="column_name operator value [logical_operator ...]"
+```
+
+**Supported Operators**:
+- **Equality**: `==`, `!=`
+- **Comparison**: `>`, `<`, `>=`, `<=`
+- **Membership**: `in`, `not in`
+- **Logical**: `and`, `or`
+- **Grouping**: `()` for precedence
+
+**Valid Examples**:
+```python
+# Simple equality
+"cell_type == 'neuron'"
+
+# List membership
+"tissue in ['kidney', 'liver']"
+
+# Complex conditions
+"cell_type == 'neuron' and tissue in ['brain', 'spinal cord']"
+
+# Numeric comparisons
+"n_genes > 500 and total_counts < 10000"
+```
+
+### 🔧 Recommendation: Replace Regex Parser
+
+The current regex-based parsing (`enhancer.py:447-483`) should be **replaced with proper SOMA syntax validation**:
+
+1. **Use AST parsing** instead of regex to validate query structure
+2. **Leverage existing SOMA validation** by testing queries against dummy DataFrames
+3. **Add syntax validation** before executing expensive SPARQL queries
+4. **Support full SOMA syntax** rather than limited regex patterns
+
+This would eliminate the major bug risk from malformed query generation and ensure compatibility with the official SOMA specification.
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diff --git a/performance_bottleneck_analysis.md b/performance_bottleneck_analysis.md
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+# Performance Bottleneck Analysis: Label Resolution
+
+## 🔍 Test Results Summary
+
+**Test Case**: `get_subclasses('astrocyte', 'cell_type')`
+
+### Critical Performance Findings
+
+| Operation | Time (seconds) | Percentage of Total |
+|-----------|----------------|-------------------|
+| **Label Resolution** | **40.49s - 53.53s** | **~99%** |
+| Subclass Query | 0.42s - 0.55s | ~1% |
+| **TOTAL** | **~41-54s** | **100%** |
+
+### 🐛 Root Cause: Complex SPARQL Label Resolution Query
+
+The bottleneck is in `OntologyExtractor.get_ontology_id_from_label()` at **lines 207-246** of `enhancer.py`.
+
+**The Problematic Query Structure**:
+```sparql
+SELECT DISTINCT ?term WHERE {
+    # 5 UNION clauses searching across multiple synonym types
+    { ?term rdfs:label ?label . FILTER(LCASE(?label) = LCASE("astrocyte")) }
+    UNION
+    { ?term oboInOwl:hasExactSynonym ?synonym . FILTER(LCASE(?synonym) = LCASE("astrocyte")) }
+    UNION
+    { ?term oboInOwl:hasRelatedSynonym ?synonym . FILTER(LCASE(?synonym) = LCASE("astrocyte")) }
+    UNION
+    { ?term oboInOwl:hasBroadSynonym ?synonym . FILTER(LCASE(?synonym) = LCASE("astrocyte")) }
+    UNION
+    { ?term oboInOwl:hasNarrowSynonym ?synonym . FILTER(LCASE(?synonym) = LCASE("astrocyte")) }
+
+    FILTER(STRSTARTS(STR(?term), "http://purl.obolibrary.org/obo/CL_"))
+}
+```
+
+### Why This Query Is Slow
+
+1. **5 UNION Operations**: Each UNION clause requires a separate scan
+2. **String Functions**: `LCASE()` and `STRSTARTS()` are expensive on large datasets
+3. **Multiple Property Searches**: Searching across 5 different synonym properties
+4. **Full Graph Scan**: Without proper indexes, this scans the entire knowledge graph
+
+### Performance Comparison
+
+```
+Direct SPARQL Queries:
+├── Label Resolution: 40.49s - 53.53s  ⚠️  BOTTLENECK
+└── Subclass Lookup:  0.42s - 0.55s   ✅  FAST
+
+Workflow Impact:
+├── With Labels:    ~41s total (99% label resolution)
+└── With Direct ID: ~0.4s total (100x faster!)
+```
+
+## 💡 Optimization Recommendations
+
+### Immediate Fixes (High Impact)
+
+1. **Optimize SPARQL Query** - Rewrite to use more efficient patterns:
+   ```sparql
+   # Instead of 5 UNIONs, use a single pattern with property paths
+   SELECT DISTINCT ?term WHERE {
+     ?term (rdfs:label|oboInOwl:hasExactSynonym|oboInOwl:hasRelatedSynonym|
+            oboInOwl:hasBroadSynonym|oboInOwl:hasNarrowSynonym) ?labelValue .
+     FILTER(LCASE(STR(?labelValue)) = LCASE("astrocyte"))
+     FILTER(STRSTARTS(STR(?term), "http://purl.obolibrary.org/obo/CL_"))
+   }
+   ```
+
+2. **Add Local Caching Layer**:
+   ```python
+   @lru_cache(maxsize=1000)
+   def get_ontology_id_from_label_cached(label, category, organism=None):
+       # Cache resolved label->ID mappings
+   ```
+
+3. **Implement Request Timeouts**:
+   ```python
+   # In SPARQLClient.__init__
+   self.sparql.setTimeout(30)  # 30-second timeout
+   ```
+
+### Medium-Term Solutions
+
+1. **Pre-build Label Index**: Create a local SQLite database with label→ID mappings
+2. **Batch Processing**: Resolve multiple labels in a single SPARQL query
+3. **Fallback Strategy**: Try exact label match first, then synonyms only if needed
+
+### Long-Term Architecture Changes
+
+1. **Local Ontology Cache**: Download and index commonly used ontologies locally
+2. **Async Processing**: Use asyncio for parallel SPARQL queries
+3. **Smart Defaults**: Provide common cell type mappings as built-in constants
+
+## 📊 Impact Analysis
+
+**Current User Experience**:
+- Simple query: 40+ seconds (unusable)
+- Multiple terms: 2+ minutes per term (completely unusable)
+
+**After Optimization**:
+- Target: <5 seconds for label resolution
+- Best case: <1 second with proper caching
+- Improvement: **8-40x speedup**
+
+## 🎯 Recommended Implementation Priority
+
+1. **CRITICAL**: Add SPARQL timeout (5 minutes to implement)
+2. **HIGH**: Optimize SPARQL query structure (2 hours)
+3. **HIGH**: Add LRU caching for label resolution (1 hour)
+4. **MEDIUM**: Build label→ID lookup table (1 day)
+5. **LOW**: Implement async processing (1 week)
+
+The **40+ second delay** for a single label lookup makes the current system unusable for production. The label resolution SPARQL query needs immediate optimization.
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diff --git a/performance_test.py b/performance_test.py
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+#!/usr/bin/env python3
+"""
+Performance testing script to identify bottlenecks in OntologyExtractor operations.
+Focus on testing the 'astrocyte' label resolution and subclass queries.
+"""
+
+import time
+import logging
+from cxg_query_enhancer import OntologyExtractor, SPARQLClient
+
+# Configure logging to see timing information
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    handlers=[logging.StreamHandler()],
+)
+
+logger = logging.getLogger(__name__)
+
+
+def time_function(func_name, func, *args, **kwargs):
+    """Time a function and log results."""
+    logger.info(f"🔍 Starting {func_name}...")
+    start_time = time.time()
+
+    try:
+        result = func(*args, **kwargs)
+        end_time = time.time()
+        duration = end_time - start_time
+
+        logger.info(f"✅ {func_name} completed in {duration:.2f} seconds")
+        return result, duration
+    except Exception as e:
+        end_time = time.time()
+        duration = end_time - start_time
+        logger.error(f"❌ {func_name} failed after {duration:.2f} seconds: {e}")
+        return None, duration
+
+
+def test_label_resolution_bottleneck():
+    """Test the specific bottleneck for 'astrocyte' label resolution."""
+    logger.info("=" * 60)
+    logger.info("PERFORMANCE TEST: Label Resolution Bottleneck Analysis")
+    logger.info("=" * 60)
+
+    # Initialize components
+    sparql_client = SPARQLClient()
+    extractor = OntologyExtractor(sparql_client)
+
+    # Test case: 'astrocyte' label
+    test_label = "astrocyte"
+    test_category = "cell_type"
+
+    logger.info(f"Testing with label: '{test_label}', category: '{test_category}'")
+
+    # Step 1: Test get_ontology_id_from_label (suspected bottleneck)
+    resolved_id, duration1 = time_function(
+        "get_ontology_id_from_label",
+        extractor.get_ontology_id_from_label,
+        test_label, test_category
+    )
+
+    if resolved_id:
+        logger.info(f"📍 Resolved '{test_label}' to ID: {resolved_id}")
+
+        # Step 2: Test get_subclasses with the resolved ID
+        subclasses, duration2 = time_function(
+            "get_subclasses (with resolved ID)",
+            extractor.get_subclasses,
+            resolved_id, test_category
+        )
+
+        if subclasses:
+            logger.info(f"📊 Found {len(subclasses)} subclasses")
+            logger.info("First 3 subclasses:")
+            for i, subclass in enumerate(subclasses[:3]):
+                logger.info(f"  {i+1}. {subclass['ID']} - {subclass['Label']}")
+
+        # Step 3: Compare with direct ID input (should be faster)
+        logger.info("\n" + "=" * 40)
+        logger.info("COMPARISON: Direct ID vs Label Input")
+        logger.info("=" * 40)
+
+        direct_subclasses, duration3 = time_function(
+            "get_subclasses (direct ID input)",
+            extractor.get_subclasses,
+            resolved_id, test_category
+        )
+
+        # Summary
+        logger.info("\n" + "=" * 40)
+        logger.info("PERFORMANCE SUMMARY")
+        logger.info("=" * 40)
+        logger.info(f"1. Label Resolution:     {duration1:.2f}s")
+        logger.info(f"2. Subclasses (label):   {duration2:.2f}s")
+        logger.info(f"3. Subclasses (direct):  {duration3:.2f}s")
+        logger.info(f"Total (label workflow):  {duration1 + duration2:.2f}s")
+        logger.info(f"Direct ID speedup:       {(duration2/duration3):.1f}x faster")
+
+        # Identify bottleneck
+        if duration1 > duration2:
+            logger.warning("🐛 BOTTLENECK IDENTIFIED: Label resolution is the slowest step!")
+        else:
+            logger.warning("🐛 BOTTLENECK IDENTIFIED: Subclass retrieval is the slowest step!")
+
+    else:
+        logger.error("❌ Could not resolve label - cannot continue with subclass testing")
+
+
+def test_sparql_query_components():
+    """Test individual SPARQL query components to identify specific bottlenecks."""
+    logger.info("\n" + "=" * 60)
+    logger.info("DETAILED SPARQL QUERY ANALYSIS")
+    logger.info("=" * 60)
+
+    sparql_client = SPARQLClient()
+
+    # Test 1: Simple label resolution query (what get_ontology_id_from_label does)
+    label_query = """
+    PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
+    PREFIX obo: <http://purl.obolibrary.org/obo/>
+    PREFIX oboInOwl: <http://www.geneontology.org/formats/oboInOwl#>
+
+    SELECT DISTINCT ?term
+    WHERE {
+        # Match main label
+        {
+            ?term rdfs:label ?label .
+            FILTER(LCASE(?label) = LCASE("astrocyte"))
+        }
+        UNION
+        # Match exact synonyms
+        {
+            ?term oboInOwl:hasExactSynonym ?synonym .
+            FILTER(LCASE(?synonym) = LCASE("astrocyte"))
+        }
+        UNION
+        # Match related synonyms
+        {
+            ?term oboInOwl:hasRelatedSynonym ?synonym .
+            FILTER(LCASE(?synonym) = LCASE("astrocyte"))
+        }
+        UNION
+        # Match broad synonyms
+        {
+            ?term oboInOwl:hasBroadSynonym ?synonym .
+            FILTER(LCASE(?synonym) = LCASE("astrocyte"))
+        }
+        UNION
+        # Match narrow synonyms
+        {
+            ?term oboInOwl:hasNarrowSynonym ?synonym .
+            FILTER(LCASE(?synonym) = LCASE("astrocyte"))
+        }
+        FILTER(STRSTARTS(STR(?term), "http://purl.obolibrary.org/obo/CL_"))
+    }
+    LIMIT 1
+    """
+
+    result1, duration1 = time_function(
+        "Direct label resolution SPARQL",
+        sparql_client.query,
+        label_query
+    )
+
+    # Test 2: Subclasses query (assumes astrocyte is CL:0000127)
+    subclass_query = """
+    PREFIX obo: <http://purl.obolibrary.org/obo/>
+    PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
+
+    SELECT DISTINCT ?term (STR(?term_label) as ?label)
+    WHERE {
+    VALUES ?inputTerm { obo:CL_0000127 }
+
+    {
+        ?term rdfs:subClassOf ?inputTerm .
+    }
+    UNION
+    {
+        ?term obo:BFO_0000050 ?inputTerm .
+    }
+
+    ?term rdfs:label ?term_label .
+    FILTER(STRSTARTS(STR(?term), "http://purl.obolibrary.org/obo/CL_"))
+    }
+    LIMIT 1000
+    """
+
+    result2, duration2 = time_function(
+        "Direct subclasses SPARQL",
+        sparql_client.query,
+        subclass_query
+    )
+
+    logger.info("\n" + "=" * 40)
+    logger.info("SPARQL QUERY TIMING COMPARISON")
+    logger.info("=" * 40)
+    logger.info(f"Label resolution query:  {duration1:.2f}s")
+    logger.info(f"Subclasses query:        {duration2:.2f}s")
+
+    if duration1 > duration2:
+        logger.warning("🐛 CONFIRMED: Label resolution SPARQL is the bottleneck!")
+        logger.info("💡 RECOMMENDATION: Cache label-to-ID mappings or use direct IDs when possible")
+    else:
+        logger.warning("🐛 UNEXPECTED: Subclasses query is slower than label resolution")
+
+
+if __name__ == "__main__":
+    logger.info("🚀 Starting Performance Analysis for 'astrocyte' label...")
+
+    try:
+        # Test the full workflow
+        test_label_resolution_bottleneck()
+
+        # Test individual SPARQL components
+        test_sparql_query_components()
+
+        logger.info("\n🎯 Performance testing completed!")
+
+    except Exception as e:
+        logger.error(f"💥 Performance test failed: {e}", exc_info=True)
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