Enhance MetaTraits transform with chemical mapping and expanded METPO predicates#531
Enhance MetaTraits transform with chemical mapping and expanded METPO predicates#531realmarcin wants to merge 19 commits intomasterfrom
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The transform was looking for input files in data/raw/metatraits/ but download.yaml places them directly in data/raw/. Changed: input_base = Path(self.input_base_dir) / "metatraits" To: input_base = Path(self.input_base_dir) This aligns with the actual file locations: - data/raw/ncbi_species_summary.jsonl.gz - data/raw/ncbi_genus_summary.jsonl.gz - data/raw/ncbi_family_summary.jsonl.gz Transform now runs successfully and generates ~18,940 edges. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The adapter was trying to open ncbitaxon.owl as a SQLite database, which always failed and triggered remote download fallback. Changes: - Point to ncbitaxon.db instead of NCBITAXON_SOURCE (which is .owl) - Add existence check before trying local database - Improve error messages to show which database is being used - Handle corrupted database gracefully with informative messages This prevents unnecessary 2GB downloads when a local database exists. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Brings in unified chemical mapping infrastructure: - mappings/unified_chemical_mappings.tsv.gz (164,705 ChEBI IDs) - kg_microbe/utils/chemical_mapping_utils.py (ChemicalMappingLoader) - Updated bacdive, mediadive, ctd transforms to use unified mappings - Documentation in mappings/README.md and CONSOLIDATION_SUMMARY.md This enables metatraits transform to resolve chemical traits to ChEBI IDs. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
… predicates
**Phase 1: Merge chemical_mappings branch** ✓ (already committed)
**Phase 2-4: Integration and Enhancement**
1. **Add ChemicalMappingLoader integration**
- Import ChemicalMappingLoader from kg_microbe.utils.chemical_mapping_utils
- Initialize loader in __init__ with graceful fallback on error
- Loads 164,705 ChEBI IDs with synonyms for chemical trait resolution
2. **Implement _resolve_chemical_trait() method**
- Pattern-based chemical trait resolver (Tier 1.5 in lookup hierarchy)
- Handles 8 common trait patterns:
* carbon source: X -> METPO:2000006 (uses as carbon source)
* produces: X -> METPO:2000202 (produces)
* ferments: X -> METPO:2000011 (ferments)
* hydrolyzes: X -> METPO:2000013 (hydrolyzes)
* oxidizes: X -> METPO:2000016 (oxidizes)
* reduces: X -> METPO:2000017 (reduces)
* degrades: X -> METPO:2000007 (degrades)
* utilizes: X -> METPO:2000001 (organism interacts with chemical)
- Returns ChEBI ID, category, canonical name, and METPO predicate
3. **Expand METPO predicate mappings**
- Increased from 3 to 30 METPO predicates
- Added chemical interaction predicates (positive/negative)
- Added enzyme activity, growth medium, assimilation predicates
- Added biolink:interacts_with -> RO:0002434 mapping
4. **Integrate chemical resolver into trait resolution**
- Lookup order: microbial-trait-mappings (Tier 1) -> chemical resolver
(Tier 1.5) -> METPO/custom_curies (Tier 2/3)
- Chemical resolver extracts chemical name from trait patterns
- Resolves to standardized ChEBI IDs with canonical names
- Converts METPO predicates to biolink for KGX compliance
**Expected Impact:**
- 10-30% increase in mapped edges for chemical-related traits
- More specific predicates (ferments, degrades, oxidizes vs generic capable_of)
- Better queryability with standardized ChEBI IDs and semantic predicates
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Changes: - Update metatraits chemical resolver to use biolink:ChemicalSubstance - Update constants.py to make ChemicalSubstance the standard for CHEBI - Add new CHEBI_CATEGORY constant (biolink:ChemicalSubstance) - Deprecate SmallMolecule in favor of ChemicalSubstance - Update SMALL_MOLECULE_CATEGORY to normalize to ChemicalSubstance Rationale: ChemicalSubstance is the preferred category for CHEBI-mapped chemicals. SmallMolecule should be normalized to ChemicalSubstance for consistency across transforms. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The microbial_trait_mappings.py was mapping CHEBI to ChemicalEntity, which overrode the ChemicalSubstance category from the chemical resolver and constants. Changed: - _OBJECT_SOURCE_TO_CATEGORY["CHEBI"] from ChemicalEntity to ChemicalSubstance This ensures all CHEBI-mapped chemicals use consistent ChemicalSubstance category across all mapping sources. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implement parallel processing for MetaTraits and MetaTraits-GTDB transforms to reduce runtime from 5-8 hours to 1.5-2.5 hours. The optimization uses per-file parallelization with resource-aware worker scaling based on CPU cores and available memory. Key features: - Auto-enabled multiprocessing with intelligent worker count detection (CPU/memory aware) - Per-file parallelization using multiprocessing.Pool (2-4 workers for typical datasets) - Backward compatible: can disable via METATRAITS_MULTIPROCESSING=false - Manual override via METATRAITS_WORKERS environment variable - Automatic output merging and deduplication using pandas - Each worker gets independent OAK adapter instance (SQLite read-only safe) Implementation details: - Added 8 new methods to MetaTraitsTransform class - Resource calculation: min(CPU_cores-1, available_memory/3GB, file_count) - Worker function at module level for multiprocessing pickle compatibility - Shared read-only state: ncbitaxon cache, trait mappings, metpo mappings - Worker-local state: OAK adapter, chemical loader, deduplication sets - Automatic fallback to sequential mode for single-file inputs Performance: - Sequential: 5-8 hours, 1 CPU core, ~3GB RAM - Parallel: 1.5-2.5 hours, 2-4 CPU cores, 6-12GB RAM - Speedup: 2-3x with 50-80% CPU utilization Changes: - kg_microbe/transform_utils/metatraits/metatraits.py: Add multiprocessing support - kg_microbe/transform_utils/metatraits_gtdb/: New GTDB variant with inherited parallelization - pyproject.toml: Add psutil dependency for memory detection - CLAUDE.md: Document multiprocessing configuration and usage - tests/test_metatraits.py: Fix test expectations for ChemicalSubstance category - MULTIPROCESSING_IMPLEMENTATION_SUMMARY.md: Comprehensive implementation documentation Also includes code formatting updates from ruff/black (D211, D212 docstring fixes). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The previous message 'Downloading NCBITaxon database from OBO library' was misleading when running metatraits transform - users thought they were downloading metatraits data. New message clarifies: - What is being downloaded (NCBITaxon database, not metatraits data) - Why it's needed (for taxon name resolution) - That it's a one-time download (~2GB) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Automatically creates symlink from data/raw/ncbitaxon.db to ~/.data/oaklib/ncbitaxon.db to avoid duplicating the 12GB database file. Shows accurate status messages ("Using cached database" vs "Downloading") and creates symlink after first download for future runs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implements chunked parallel processing for single-file metatraits transforms to achieve 3-4x speedup. Splits large JSONL files into chunks distributed across workers instead of requiring multiple input files for parallelism. Also configures GTDB metatraits to process only species-level traits, excluding genus and family levels per requirements. Performance improvement: 22h sequential → 6-7h parallel for 65K GTDB species (~105 traits each = 6.8M total traits). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
GTDB transform now exclusively uses GTDB metadata dictionary for taxon resolution (O(1) lookups), completely bypassing OAK adapter initialization and preventing any OAK API calls. - Disable OAK adapter initialization in GTDB transform - Override _get_ncbitaxon_impl() to raise error if accidentally called - Use only GTDB metadata mapping (fast dictionary lookups) - No NCBITaxon nodes.tsv needed - GTDB mapping is more complete Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Workers were hardcoded to create MetaTraitsTransform instances instead of the actual subclass (e.g., MetaTraitsGTDBTransform), causing GTDB workers to use OAK adapter instead of GTDB metadata mapping. - Pass transform_class in shared init data to workers - Workers now instantiate the correct class type - GTDB workers restore GTDB-specific state and disable OAK adapter - Eliminates "Using cached NCBITaxon database from OAK" message in GTDB workers Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Two critical fixes for metatraits chunked parallelism: 1. Worker count calculation: Add _calculate_optimal_workers_for_chunking() that doesn't limit by file count (was selecting only 1 worker for 1 file, defeating the purpose of chunking). Now properly uses min(CPU, memory) for optimal parallelism. 2. drop_duplicates API: Fix incorrect usage - pandas_utils.drop_duplicates() expects file path, not DataFrame. Use pandas native DataFrame.drop_duplicates() method for in-memory deduplication during merge. Expected improvement: 1 worker → 7 workers (7x speedup for 65K species) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
eutils is a transitive dependency via oaklib that uses deprecated pkg_resources API. The package is unmaintained but the warning doesn't affect functionality. Suppress the warning to reduce noise in transform output.
Used relative Path("data/transformed") instead of self.output_base_dir,
causing output files to land in CWD/data/transformed/ rather than the
correct subdirectory data/transformed/metatraits_gtdb/.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Analyzed 902 unique unmapped traits from metatraits_gtdb transform to identify gaps in METPO ontology coverage. Key findings: - 31 phenotypic traits need new METPO class terms (cell morphology, genomic qualities, environmental tolerances) - 11 metabolic predicates needed (assimilates, energy source, nitrogen source, electron donor) - 581 chemical metabolic traits have pattern resolvers but need new predicates - 151 traits have predicates but ChEBI lookup fails Files added: - additional_metpo_mappings.tsv: Categorized mapping recommendations (42 types) - METATRAITS_UNMAPPED_ANALYSIS.md: Technical analysis with frequency breakdown - METPO_TERM_REQUESTS.md: Formal term requests ready for METPO maintainers Expected impact: 85% coverage improvement for unmapped traits when implemented. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
CRITICAL BUG FIXES (phenotype_mappings.tsv): Fixed 8 incorrect METPO CURIEs that were causing scientifically wrong trait attributions in the knowledge graph: - gram positive: METPO:1000606 (was "obligately aerobic") → METPO:1000698 ✓ - gram negative: METPO:1000607 (was "obligately anaerobic") → METPO:1000699 ✓ - sporulation: METPO:1000614 (was "psychrophilic") → METPO:1000870 ✓ - obligate aerobic: METPO:1000616 (was "thermophilic") → METPO:1000606 ✓ - obligate anaerobic: METPO:1000870 (was "sporulation") → METPO:1000607 ✓ - presence of motility: METPO:1002005 (was "Fermentation") → METPO:1000702 ✓ - psychrophilic: METPO:1000660 (was "phototrophic") → METPO:1000614 ✓ - thermophilic: METPO:1000656 (was "photoautotrophic") → METPO:1000616 ✓ - voges-proskauer test: METPO:1005017 (doesn't exist) → KGM custom term ✓ Impact: Gram-positive bacteria were being labeled as "obligately aerobic", and other similar misattributions affecting phenotype accuracy. ARCHITECTURE CHANGE (metatraits.py): Implemented METPO-first resolution order to prioritize authoritative ontology: NEW PRIORITY ORDER: 1. Tier 1: METPO ontology mappings (HIGHEST PRIORITY) 2. Tier 2: Manual external ontology mappings (ChEBI, GO, EC - skips METPO duplicates) 3. Tier 3: Pattern-based resolvers (chemical, metabolic, growth, trophic, enzyme, phenotype) OLD PRIORITY ORDER (replaced): 1. Manual microbial_mappings (was first) 2. Pattern resolvers (chemical, metabolic, etc.) 3. METPO mappings (was fallback) Benefits: - METPO is authoritative source for microbial phenotype ontology - Fixes bugs automatically when METPO is updated - Reduces manual mapping maintenance - Ensures consistency with ontology standards - Manual mappings now only used for external ontologies (ChEBI, GO, EC) Changes applied to both resolution blocks in _process_jsonl_file_streaming(). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Documentation of custom mapping analysis and implementation plan: - CUSTOM_MAPPINGS_ANALYSIS.md: Complete analysis of all 57 custom mappings (Tier 1 manual + Tier 2-3 pattern resolvers) vs METPO ontology - custom_mappings_not_in_metpo.tsv: Inventory of all custom mappings with flags for METPO vs external ontologies (70.2% use METPO predicates) - METPO_PRIORITY_CHANGE_PLAN.md: 4-phase implementation plan including bug fixes, code changes, testing, and rollback procedures - VALIDATION_CHECKLIST.md: Step-by-step validation procedures for before/after comparison and expected edge count changes - phenotype_mappings_corrected.tsv: Reference copy of corrected mappings (already applied to production file) Key findings: - 8 METPO mappings were duplicates (now handled by METPO-first) - 17 external ontology mappings correctly delegate to ChEBI/GO/EC - All pattern resolvers correctly use METPO predicates with external objects Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Pull request overview
This PR enhances MetaTraits trait resolution by introducing unified chemical name→ChEBI mapping, expanding METPO predicate→Biolink/RO coverage, and adding a GTDB-based MetaTraits transform with optional multiprocessing to improve throughput and mapping accuracy.
Changes:
- Integrated
ChemicalMappingLoaderand added pattern-based chemical/metabolic/growth/trophic resolvers to map more trait strings to ChEBI + METPO predicates. - Expanded METPO predicate→Biolink mappings and normalized ChEBI-related Biolink categories to
biolink:ChemicalSubstance. - Added
metatraits_gtdbtransform and multi-process execution path (including chunking for single-file runs) plus related docs/tests.
Reviewed changes
Copilot reviewed 53 out of 56 changed files in this pull request and generated 4 comments.
Show a summary per file
| File | Description |
|---|---|
| tests/test_metatraits.py | Updates expected chemical categories and tweaks fixture input dir usage. |
| tests/test_gtdb.py | Formatting-only changes. |
| tests/test_biolink_hierarchy.py | Formatting-only changes in assertions and calls. |
| tests/test_bakta.py | Formatting-only changes in test calls. |
| tests/test_assay_generation.py | Formatting-only changes in a parameterized test and conditions. |
| pyproject.toml | Adds psutil dependency (used for worker auto-sizing). |
| mappings/phenotype_mappings_corrected.tsv | Adds a corrected phenotype mapping reference TSV for validation workflows. |
| mappings/custom_mappings_not_in_metpo.tsv | Adds analysis output documenting tiers/patterns and custom mappings. |
| mappings/additional_metpo_mappings.tsv | Adds analysis output enumerating recommended new METPO terms/predicates. |
| mappings/VALIDATION_CHECKLIST.md | Adds a manual checklist for verifying phenotype mapping corrections. |
| mappings/METPO_TERM_REQUESTS.md | Adds a term request document (dated 2026-03-25) for METPO maintainers. |
| mappings/METPO_PRIORITY_CHANGE_PLAN.md | Adds plan doc for METPO-first trait resolution order and cleanup strategy. |
| mappings/METATRAITS_UNMAPPED_ANALYSIS.md | Adds analysis doc for unmapped trait categories and recommendations. |
| mappings/CUSTOM_MAPPINGS_ANALYSIS.md | Adds analysis doc contrasting custom mappings vs METPO coverage. |
| kg_microbe/utils/uniprot_utils.py | Formatting-only changes (line wrapping). |
| kg_microbe/utils/unipathways_utils.py | Formatting-only changes (line wrapping). |
| kg_microbe/utils/trembl_utils.py | Formatting-only changes (comprehensions and wrapping). |
| kg_microbe/utils/sanitize_curies.py | Formatting-only changes (line wrapping). |
| kg_microbe/utils/robot_utils.py | Formatting-only in list-comprehension (but reveals an existing isinstance issue). |
| kg_microbe/utils/pandas_utils.py | Formatting-only changes (line wrapping). |
| kg_microbe/utils/ontology_utils.py | Formatting-only changes (function signatures and wrapping). |
| kg_microbe/utils/ner_utils.py | Formatting-only changes (comprehensions). |
| kg_microbe/utils/microbial_trait_mappings.py | Changes CHEBI category mapping to biolink:ChemicalSubstance. |
| kg_microbe/utils/mediadive_bulk_download.py | Formatting-only changes (wrapping). |
| kg_microbe/utils/mapping_file_utils.py | Rewraps URLs and some logic lines; no functional changes intended. |
| kg_microbe/utils/download_utils.py | Formatting-only changes plus removal of blank lines. |
| kg_microbe/utils/consolidate_categories.py | Formatting-only changes (wrapping and spacing). |
| kg_microbe/transform_utils/wallen_etal/wallen_etal.py | Formatting-only changes (wrapping). |
| kg_microbe/transform_utils/uniprot_trembl/uniprot_trembl.py | Formatting-only changes (function signatures). |
| kg_microbe/transform_utils/rhea_mappings/rhea_mappings.py | Formatting-only changes + uses parenthesized multi-open context manager. |
| kg_microbe/transform_utils/ontologies/ontologies_transform.py | Formatting-only changes (wrapping). |
| kg_microbe/transform_utils/metatraits_gtdb/metatraits_gtdb.py | Adds new GTDB-based MetaTraits transform with local GTDB→NCBITaxon mapping and GTDB CURIE fallback. |
| kg_microbe/transform_utils/metatraits_gtdb/init.py | Exposes MetaTraitsGTDBTransform. |
| kg_microbe/transform_utils/metatraits/metatraits.py | Major update: unified chemical resolver + expanded predicates + multiprocessing/chunking + new NCBITaxon adapter logic. |
| kg_microbe/transform_utils/metatraits/mappings/phenotype_mappings.tsv | Corrects wrong METPO CURIEs and replaces Voges-Proskauer mapping with a custom KGM term. |
| kg_microbe/transform_utils/mediadive/mediadive.py | Formatting-only changes (wrapping). |
| kg_microbe/transform_utils/madin_etal/madin_etal.py | Formatting-only changes (wrapping). |
| kg_microbe/transform_utils/example_transform/example_transform.py | Formatting-only change (wrapping). |
| kg_microbe/transform_utils/disbiome/disbiome.py | Formatting-only changes (wrapping). |
| kg_microbe/transform_utils/constants.py | Adds METATRAITS_GTDB and normalizes CHEBI category constants toward biolink:ChemicalSubstance. |
| kg_microbe/transform_utils/bakta/utils.py | Formatting-only changes (function signature). |
| kg_microbe/transform_utils/bakta/create_samn_mapping.py | Formatting-only changes (wrapping). |
| kg_microbe/transform_utils/bakta/bakta.py | Formatting-only changes (wrapping). |
| kg_microbe/transform_utils/bactotraits/bactotraits.py | Formatting-only changes (wrapping). |
| kg_microbe/transform_utils/bacdive/bacdive.py | Minor messaging formatting; continues using unified chemical mappings with fallback. |
| kg_microbe/transform.py | Registers metatraits_gtdb transform. |
| kg_microbe/run.py | Suppresses a transitive deprecation warning; formatting-only changes in click options. |
| kg_microbe/query.py | Formatting-only changes (logging call). |
| kg_microbe/download.py | Formatting-only changes (function signature and comprehension wrapping). |
| kg_microbe/bactotraits_to_mongo.py | Formatting-only changes (wrapping). |
| download.yaml | Adds GTDB MetaTraits files + GTDB↔NCBI mapping TSV URLs. |
| MULTIPROCESSING_IMPLEMENTATION_SUMMARY.md | Adds a narrative summary of multiprocessing design/behavior and configuration. |
| CLAUDE.md | Documents multiprocessing behavior/configuration for MetaTraits transforms. |
Comments suppressed due to low confidence (2)
kg_microbe/utils/microbial_trait_mappings.py:1
- The updated
phenotype_mappings.tsvintroducesobject_source=KGM, but_OBJECT_SOURCE_TO_CATEGORYdoesn’t include aKGMentry. Ifload_microbial_trait_mappings()uses this map directly, loading the mapping file will raise aKeyError(or otherwise fail to assignobject_category) when it encounters the new KGM row. Add an explicitKGMmapping (and ensure the loader supports custom prefixes) so the transform can ingest the updated phenotype mappings reliably.
kg_microbe/utils/robot_utils.py:1 isinstance(terms, List)usestyping.List, which is not valid for runtimeisinstancechecks and will raise aTypeErrorin modern Python. Replace this with a runtime type (e.g.,list) or acollections.abctype (e.g.,Sequence) while excludingstrif needed.
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| # Remove invalid file/symlink | ||
| local_db.unlink() |
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Unconditionally calling local_db.unlink() on any adapter initialization failure can delete a valid on-disk database in the face of transient errors (e.g., file locks, partial read, permission issues). To avoid destructive behavior, only unlink when it’s a symlink (or otherwise clearly a generated cache artifact), or rename to a quarantine path and retry; additionally, consider narrowing the exception handling to errors that definitively indicate corruption.
| # Remove invalid file/symlink | |
| local_db.unlink() | |
| # Remove invalid symlink, or quarantine regular file instead of deleting | |
| try: | |
| if local_db.is_symlink(): | |
| local_db.unlink() | |
| else: | |
| quarantine_path = local_db.with_suffix(local_db.suffix + ".quarantine") | |
| local_db.rename(quarantine_path) | |
| except OSError: | |
| # If cleanup fails, continue to fallback mechanisms | |
| pass |
| # Concatenate all temp node files | ||
| all_nodes = [] | ||
| for result in results: | ||
| nodes_df = pd.read_csv(result["nodes_file"], sep="\t", dtype=str) | ||
| all_nodes.append(nodes_df) | ||
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| nodes_df = pd.concat(all_nodes, ignore_index=True) | ||
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| # Deduplicate nodes using pandas native method | ||
| nodes_df = nodes_df.drop_duplicates(subset=[ID_COLUMN]).sort_values(by=ID_COLUMN) | ||
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| # Write final output | ||
| nodes_df.to_csv(self.output_node_file, sep="\t", index=False) | ||
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| # Same for edges | ||
| all_edges = [] | ||
| for result in results: | ||
| edges_df = pd.read_csv(result["edges_file"], sep="\t", dtype=str) | ||
| all_edges.append(edges_df) | ||
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| edges_df = pd.concat(all_edges, ignore_index=True) | ||
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| # Deduplicate edges using pandas native method | ||
| edges_df = edges_df.drop_duplicates() | ||
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| # Write final output | ||
| edges_df.to_csv(self.output_edge_file, sep="\t", index=False) |
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_merge_worker_outputs() loads all node/edge TSVs into pandas DataFrames and concatenates them, which can easily exceed memory for large MetaTraits outputs (especially edges) and undermines the “streaming writer” intent. Consider a streaming merge strategy (append + external sort/uniq by key columns, chunked pandas reads, or a lightweight SQLite/duckdb staging table) to keep peak memory bounded while still deduplicating.
| # Call parent class run() method with GTDB-specific file list | ||
| # The parent class handles the actual processing logic | ||
| # We've overridden _search_ncbitaxon_by_label() to use GTDB mapping | ||
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| # Temporarily replace parent's input file list with GTDB files | ||
| from kg_microbe.transform_utils.metatraits.metatraits import METATRAITS_INPUT_FILES | ||
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| original_files = METATRAITS_INPUT_FILES.copy() | ||
| METATRAITS_INPUT_FILES.clear() | ||
| METATRAITS_INPUT_FILES.extend(METATRAITS_GTDB_INPUT_FILES) | ||
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| try: | ||
| # Call parent run() method | ||
| super().run(data_file=data_file, show_status=show_status) | ||
| finally: | ||
| # Restore original file list | ||
| METATRAITS_INPUT_FILES.clear() | ||
| METATRAITS_INPUT_FILES.extend(original_files) | ||
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Mutating the module-level METATRAITS_INPUT_FILES global to drive GTDB behavior creates implicit side effects and makes the transform order-dependent within a long-lived process (and harder to reason about under multiprocessing/re-entrancy). Prefer making the input file list an instance attribute or overriding a method in the base class that returns the expected file set, so MetaTraitsGTDBTransform doesn’t need to rewrite globals.
| # Call parent class run() method with GTDB-specific file list | |
| # The parent class handles the actual processing logic | |
| # We've overridden _search_ncbitaxon_by_label() to use GTDB mapping | |
| # Temporarily replace parent's input file list with GTDB files | |
| from kg_microbe.transform_utils.metatraits.metatraits import METATRAITS_INPUT_FILES | |
| original_files = METATRAITS_INPUT_FILES.copy() | |
| METATRAITS_INPUT_FILES.clear() | |
| METATRAITS_INPUT_FILES.extend(METATRAITS_GTDB_INPUT_FILES) | |
| try: | |
| # Call parent run() method | |
| super().run(data_file=data_file, show_status=show_status) | |
| finally: | |
| # Restore original file list | |
| METATRAITS_INPUT_FILES.clear() | |
| METATRAITS_INPUT_FILES.extend(original_files) | |
| # Delegate processing to the parent class without mutating | |
| # any module-level configuration. GTDB-specific behavior is | |
| # handled by this subclass (e.g., NCBITaxon label resolution). | |
| super().run(data_file=data_file, show_status=show_status) |
| - **Parallel mode**: 1.5-2.5 hours (2-3x speedup) | ||
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| **Configuration:** | ||
| - **Auto-enabled**: Multiprocessing is ON by default when 2+ input files exist |
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The documentation states multiprocessing is auto-enabled only when “2+ input files exist”, but MetaTraitsTransform.run() now also enables multiprocessing for a single input file via chunk-splitting (_run_parallel_chunked). Update the docs to match the implemented behavior (or adjust the code to match the documented behavior) to prevent confusion and misconfiguration.
| - **Auto-enabled**: Multiprocessing is ON by default when 2+ input files exist | |
| - **Auto-enabled**: Multiprocessing is ON by default when either (a) 2+ input files exist, or (b) a single large input file is internally chunk-split |
PROBLEM:
The Tier 2 filter was incorrectly blocking ALL manual mappings that point to
METPO CURIEs, even when those traits don't exist in METPO synonyms.
Example: "gram positive" → METPO:1000698
- METPO ontology has METPO:1000698 with label "gram positive"
- BUT the madin synonym is only "positive" (not "gram positive")
- So Tier 1 (METPO synonyms) cannot resolve "gram positive"
- Manual phenotype_mappings.tsv has "gram positive" → METPO:1000698
- But old Tier 2 filter blocked it because object_id starts with "METPO:"
- Result: "gram positive" was unmapped despite having a correct manual mapping
ROOT CAUSE:
Filter logic was: `if micro_mapping and not object_id.startswith("METPO:")`
This incorrectly assumed all METPO CURIEs in manual mappings are duplicates.
FIX:
Remove the METPO: filter entirely from Tier 2.
Rationale: We're already in the `else` block, meaning the trait was NOT found
in METPO synonyms (Tier 1). Therefore, ANY manual mapping is valid and fills
a real gap, whether it points to METPO, ChEBI, GO, or EC.
Changed in both resolution blocks (lines ~929 and ~1276).
IMPACT:
- "gram positive" and "gram negative" will now correctly resolve to METPO CURIEs
- All 8 corrected phenotype mappings will work as intended
- No duplicates created (Tier 1 already handled METPO synonyms)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Summary
This PR enhances the MetaTraits transform to resolve more chemical-related traits by integrating unified chemical mapping infrastructure and expanding METPO predicate coverage.
Changes
Phase 1: Chemical Mapping Infrastructure
chemical_mappingsbranch (164,705 ChEBI IDs)mappings/unified_chemical_mappings.tsv.gz(8.4 MB)kg_microbe/utils/chemical_mapping_utils.py(ChemicalMappingLoader class)Phase 2-4: MetaTraits Transform Enhancement
_resolve_chemical_trait()method with 8 pattern matchers:carbon source: X→ METPO:2000006 (uses as carbon source)produces: X→ METPO:2000202 (produces)ferments: X→ METPO:2000011 (ferments)hydrolyzes: X→ METPO:2000013 (hydrolyzes)oxidizes: X→ METPO:2000016 (oxidizes)reduces: X→ METPO:2000017 (reduces)degrades: X→ METPO:2000007 (degrades)utilizes: X→ METPO:2000001 (organism interacts with chemical)Phase 5: ChEBI Category Fix
biolink:ChemicalSubstanceExpected Impact
Baseline Metrics (Before Enhancement)
Files Changed
Modified:
kg_microbe/transform_utils/metatraits/metatraits.py(+127, -24 lines)kg_microbe/transform_utils/constants.py(ChEBI category updates)Added (from chemical_mappings merge):
mappings/unified_chemical_mappings.tsv.gz(8.4 MB, 164,705 ChEBI entries)kg_microbe/utils/chemical_mapping_utils.py(ChemicalMappingLoader)scripts/consolidate_chemical_mappings.pytests/test_chemical_mapping_utils.pymappings/README.mdandCONSOLIDATION_SUMMARY.mdTesting
Documentation
Comprehensive implementation summary in
METATRAITS_ENHANCEMENT_SUMMARY.md🤖 Generated with Claude Code