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gbanyan d8009f1236 docs(03-04): complete subcellular localization evidence layer

- Created SUMMARY.md with full implementation details
- Updated STATE.md: progress 40%, 8/20 plans complete
- Documented 4 key decisions (evidence terminology, NULL semantics, embedded proteomics, evidence weighting)
- All verification criteria met: 17/17 tests pass, CLI functional, DuckDB integration complete

2026-02-11 19:08:01 +08:00

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Project State

Project Reference

See: .planning/PROJECT.md (updated 2026-02-11)

Core value: Produce a high-confidence, multi-evidence-backed ranked list of under-studied cilia/Usher candidate genes that is fully traceable — every gene's inclusion is explainable by specific evidence, and every gap is documented. Current focus: Phase 2 complete — ready for Phase 3

Current Position

Phase: 3 of 6 (Core Evidence Layers) Plan: 4 of 6 in current phase Status: In progress — 03-04 complete (subcellular localization) Last activity: 2026-02-11 — Completed 03-04-PLAN.md (Subcellular Localization evidence layer)

Progress: [█████░░░░░] 40.0% (8/20 plans complete across all phases)

Performance Metrics

Velocity:

Total plans completed: 8
Average duration: 4.7 min
Total execution time: 0.63 hours

By Phase:

Phase	Plans	Total	Avg/Plan
01 - Data Infrastructure	4/4	14 min	3.5 min/plan
02 - Prototype Evidence Layer	2/2	8 min	4.0 min/plan
03 - Core Evidence Layers	2/6	16 min	8.0 min/plan

Accumulated Context

Decisions

Decisions are logged in PROJECT.md Key Decisions table. Recent decisions affecting current work:

Python over R/Bioconductor for rich data integration ecosystem
Weighted rule-based scoring over ML for explainability
Public data only for reproducibility
Modular CLI scripts for flexibility during development
Virtual environment required for dependency isolation (01-01: PEP 668 externally-managed Python)
Auto-creation of directories on config load (01-01: data_dir, cache_dir field validators)
[01-02]: Warn on gene count outside 19k-22k range but don't fail (allows for Ensembl version variations)
[01-02]: HGNC success rate is primary validation gate (UniProt mapping tracked but not used for pass/fail)
[01-02]: Take first UniProt accession when multiple exist (simplifies data model)
[01-02]: Mock mygene in tests (avoids rate limits, ensures reproducibility)
[01-03]: DuckDB over SQLite for DataFrame storage (native polars/pandas integration, better analytics)
[01-03]: Provenance sidecar files alongside outputs (co-located metadata, bioinformatics standard pattern)
[01-04]: Click for CLI framework (standard Python CLI library with excellent UX)
[01-04]: Setup command uses checkpoint-restart pattern (gene universe fetch can take minutes)
[01-04]: Mock mygene in integration tests (avoids external API dependency, reproducible)
[02-01]: httpx over requests for streaming downloads (async-native, cleaner API)
[02-01]: structlog for structured logging (JSON-formatted, context-aware)
[02-01]: LOEUF normalization with inversion (lower LOEUF = more constrained = higher 0-1 score)
[02-01]: Quality flags instead of filtering (preserve all genes with measured/incomplete_coverage/no_data categorization)
[02-01]: NULL preservation pattern (unknown constraint != zero constraint, must not be conflated)
[02-01]: Lazy polars evaluation (LazyFrame until final collect() for query optimization)
[02-02]: load_to_duckdb uses CREATE OR REPLACE for idempotency (safe to re-run)
[02-02]: CLI evidence command group for extensibility (future evidence sources follow same pattern)
[02-02]: Checkpoint at table level (has_checkpoint checks DuckDB table existence)
[02-02]: Integration tests with synthetic fixtures (no external downloads, fast, reproducible)
[03-01]: Annotation tier thresholds: Well >= (20 GO AND 4 UniProt), Partial >= (5 GO OR 3 UniProt)
[03-01]: Composite annotation score weighting: GO 50%, UniProt 30%, Pathway 20%
[03-01]: NULL GO counts treated as zero for tier classification but preserved as NULL in data (conservative assumption)
[03-04]: Evidence type terminology standardized to computational (not predicted) for consistency with bioinformatics convention
[03-04]: Proteomics absence stored as False (informative negative) vs HPA absence as NULL (unknown/not tested)
[03-04]: Curated proteomics reference gene sets (CiliaCarta, Centrosome-DB) embedded as Python constants for simpler deployment
[03-04]: Computational evidence (HPA Uncertain/Approved) downweighted to 0.6x vs experimental (Enhanced/Supported, proteomics) at 1.0x

Pending Todos

None yet.

Blockers/Concerns