docs: update gitignore to track report results, add README cross-links
- Revised .gitignore to ignore raw data/cache but track data/report/ (candidates TSV/Parquet, plots, reproducibility metadata) - Added zh↔en cross-links between README.md and README.en.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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.gitignore
vendored
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.gitignore
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# Data files
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# === Raw data & intermediate files ===
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data/
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data/cache/
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data/gnomad/
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data/annotation/
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data/expression/
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data/localization/
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data/animal_models/
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data/scoring/
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# DuckDB
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# DuckDB database (large, regeneratable)
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*.duckdb
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*.duckdb
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*.duckdb.wal
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*.duckdb.wal
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# Python
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# Provenance JSON (regenerated on each run)
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*.provenance.json
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*.provenance.provenance.json
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# === Keep report results ===
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# data/report/ is NOT ignored — candidates.tsv, parquet, plots are tracked
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# === Python ===
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__pycache__/
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__pycache__/
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*.pyc
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*.pyc
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*.pyo
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*.pyo
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@@ -18,23 +31,27 @@ dist/
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build/
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build/
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.eggs/
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.eggs/
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# Testing
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# === Testing ===
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.pytest_cache/
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.pytest_cache/
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.coverage
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.coverage
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htmlcov/
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htmlcov/
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.tox/
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.tox/
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# IDE
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# === IDE ===
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.vscode/
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.vscode/
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.idea/
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.idea/
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*.swp
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*.swp
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*.swo
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*.swo
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*~
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*~
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# Provenance files (not in data/)
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# === Virtual environment ===
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/*.provenance.json
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# Virtual environment
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.venv/
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.venv/
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venv/
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venv/
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env/
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env/
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# === OS ===
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.DS_Store
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Thumbs.db
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# === Planning (internal) ===
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.planning/
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@@ -1,5 +1,7 @@
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# Usher Cilia Candidate Gene Discovery Pipeline
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# Usher Cilia Candidate Gene Discovery Pipeline
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> **[中文版 (README.md)](README.md)**
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A reproducible bioinformatics pipeline for systematic screening of candidate genes associated with Usher syndrome and ciliopathies.
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A reproducible bioinformatics pipeline for systematic screening of candidate genes associated with Usher syndrome and ciliopathies.
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This pipeline evaluates approximately 22,600 human protein-coding genes across six independent evidence layers, producing weighted composite scores and tiered candidate gene lists for downstream experimental validation.
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This pipeline evaluates approximately 22,600 human protein-coding genes across six independent evidence layers, producing weighted composite scores and tiered candidate gene lists for downstream experimental validation.
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# Usher Cilia Candidate Gene Discovery Pipeline
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# Usher Cilia Candidate Gene Discovery Pipeline
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> **[English version (README.en.md)](README.en.md)**
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一套可重現的生物資訊分析管線,用於系統性篩選與 Usher 症候群及纖毛病變(ciliopathies)相關的候選基因。
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一套可重現的生物資訊分析管線,用於系統性篩選與 Usher 症候群及纖毛病變(ciliopathies)相關的候選基因。
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本管線對人類約 22,600 個蛋白質編碼基因,透過六個獨立的證據層面進行評分與排序,最終產出分層候選基因清單,供後續實驗驗證參考。
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本管線對人類約 22,600 個蛋白質編碼基因,透過六個獨立的證據層面進行評分與排序,最終產出分層候選基因清單,供後續實驗驗證參考。
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data/report/candidates.parquet
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data/report/candidates.parquet
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data/report/candidates.provenance.yaml
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data/report/candidates.provenance.yaml
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generated_at: '2026-02-12T18:42:59.932245+00:00'
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output_files:
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- candidates.tsv
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- candidates.parquet
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statistics:
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total_candidates: 18116
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high_count: 0
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medium_count: 2151
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low_count: 15965
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column_count: 22
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column_names:
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- gene_id
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- gene_symbol
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- gnomad_score
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- expression_score
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- annotation_score
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- localization_score
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- animal_model_score
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- literature_score
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- evidence_count
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- available_weight
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- weighted_sum
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- composite_score
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- quality_flag
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- gnomad_contribution
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- expression_contribution
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- annotation_contribution
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- localization_contribution
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- animal_model_contribution
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- literature_contribution
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- confidence_tier
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- supporting_layers
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- evidence_gaps
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18117
data/report/candidates.tsv
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18117
data/report/candidates.tsv
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data/report/plots/layer_contributions.png
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data/report/plots/layer_contributions.png
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data/report/plots/score_distribution.png
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data/report/plots/score_distribution.png
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data/report/plots/tier_breakdown.png
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data/report/reproducibility.json
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data/report/reproducibility.json
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{
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"run_id": "5f00f9da-e548-4a58-b1b3-028d05c94d32",
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"timestamp": "2026-02-12T18:43:00.223842+00:00",
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"pipeline_version": "0.1.0",
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"parameters": {
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"gnomad": 0.2,
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"expression": 0.2,
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"annotation": 0.15,
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"localization": 0.15,
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"animal_model": 0.15,
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"literature": 0.15
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},
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"data_versions": {
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"ensembl_release": 113,
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"gnomad_version": "v4.1",
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"gtex_version": "v8",
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"hpa_version": "23.0"
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},
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"software_environment": {
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"python": "3.14.3",
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"polars": "1.38.1",
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"duckdb": "1.4.4"
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},
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"filtering_steps": [
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{
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"step_name": "load_scored_genes",
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"input_count": 0,
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"output_count": 0,
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"criteria": ""
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},
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{
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"step_name": "apply_tier_classification",
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"input_count": 0,
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"output_count": 0,
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"criteria": ""
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},
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{
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"step_name": "write_candidate_output",
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"input_count": 0,
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"output_count": 0,
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"criteria": ""
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},
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{
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"step_name": "generate_visualizations",
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"input_count": 0,
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"output_count": 0,
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"criteria": ""
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}
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],
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"validation_metrics": {},
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"tier_statistics": {
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"total": 18116,
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"high": 0,
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"medium": 2151,
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"low": 15965
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}
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}
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data/report/reproducibility.md
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data/report/reproducibility.md
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# Pipeline Reproducibility Report
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**Run ID:** `5f00f9da-e548-4a58-b1b3-028d05c94d32`
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**Timestamp:** 2026-02-12T18:43:00.223842+00:00
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**Pipeline Version:** 0.1.0
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## Parameters
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**Scoring Weights:**
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- gnomAD: 0.20
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- Expression: 0.20
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- Annotation: 0.15
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- Localization: 0.15
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- Animal Model: 0.15
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- Literature: 0.15
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## Data Versions
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- **ensembl_release:** 113
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- **gnomad_version:** v4.1
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- **gtex_version:** v8
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- **hpa_version:** 23.0
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## Software Environment
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- **python:** 3.14.3
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- **polars:** 1.38.1
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- **duckdb:** 1.4.4
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## Filtering Steps
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| Step | Input Count | Output Count | Criteria |
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|------|-------------|--------------|----------|
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| load_scored_genes | 0 | 0 | |
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| apply_tier_classification | 0 | 0 | |
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| write_candidate_output | 0 | 0 | |
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| generate_visualizations | 0 | 0 | |
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## Tier Statistics
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- **Total Candidates:** 18116
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- **HIGH:** 0
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- **MEDIUM:** 2151
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- **LOW:** 15965
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