--- id: gh-target-prioritization name: "target-prioritization" url: https://skills.yangsir.net/skill/gh-target-prioritization author: agents365-ai domain: science tags: ["drug-target-prioritization", "bioinformatics", "drug-discovery", "gene-prioritization", "multi-source-dossier"] install_count: 2900 rating: 4.40 (120 reviews) github: https://github.com/agents365-ai/365-skills/tree/main/plugins/target-prioritization/skills/target-prioritization --- # target-prioritization > 对差异表达分析后的候选基因列表进行多维度证据整合与优先级排序，通过自动化查询UniProt、OpenTargets、PubMed等数据库，生成包含蛋白定位、遗传学、可药性、安全性等指标的综合报告，帮助药物研发团队高效筛选最可能成功的药物靶点。 **Stats**: 2,900 installs · 4.4/5 (120 reviews) ## Before / After 对比 ### 靶点筛选与优先级排序效率 **Before**: 在没有这个技能之前，用户需要手动逐个查询UniProt、OpenTargets、PubMed等数据库，人工整合信息并计算综合评分，通常需要数天时间，且容易遗漏关键证据或出错。 **After**: 用了该Skill后，自动化并行查询5个数据库，2小时内完成所有检索、评分并生成综合报告，证据覆盖全面且结果一致。 | Metric | Before | After | Change | |---|---|---|---| | 手动耗时 | 48小时 | 2小时 | -96% | | 覆盖数据库数量 | 3个 | 5个 | +67% | | 遗漏关键证据概率 | 20% | 5% | -75% | ## Readme # Target Prioritization A multi-source drug-target due-diligence pipeline for ranked gene lists. ## When this skill triggers The user has a list of candidate genes (typically from a DE / DEG / scRNA-seq analysis) and wants a per-gene dossier across multiple evidence dimensions plus a composite re-ranking. The DE statistical rank is just the entry point; the final priority is informed by protein biology, genetics, druggability, and research maturity. Common input shapes: - A CSV with a `gene` column (DE output like `expression_table_pass_either_1s.csv`) - A plain-text gene list (one symbol per line) - A list of symbols inline in the user's message ## Output Three files inside `/`: 1. **`targets_report.md`** — one section per gene, sorted by composite score, with a short LLM-written rationale and recommended next step 2. **`targets_summary.csv`** — flat table for sorting/filtering in Excel/pandas 3. **`raw_data/.json`** — raw API responses (audit trail, reusable across future re-scorings) ## Pipeline ``` input gene list │ ▼ scripts/orchestrate.py │ ├─► fetch_uniprot.py → protein localization, surface, MHC, coding ├─► fetch_opentargets.py → tractability, approved drugs, associated │ diseases (subsumes GWAS Catalog via OT's │ integrated genetics evidence), DepMap CRISPR │ essentiality, gnomAD LOEUF / pLI constraint ├─► fetch_pubmed.py → paper counts (total + focus_disease + cell_context) ├─► fetch_hpa.py → HPA tissue / single-cell specificity + nCPM, │ expression cluster, cancer prognostics └─► fetch_chembl.py → top-potency tool compounds per gene (pIC50, IC50 nM, mechanism) — dossier-only, no score │ ▼ scripts/aggregate.py │ ▼ output_dir/ ├─ raw_data/*.json ├─ targets_summary.csv ← composite-score-ranked └─ targets_report.md ← Claude fills the rationale sections ``` ## How to invoke ```bash python3 ~/myagents/myskills/target-prioritization/scripts/orchestrate.py \ --input \ --output \ [--gene-col gene] \ [--top 50] ``` - `--input` accepts a CSV (with `--gene-col`, default `gene`), a `.txt`/`.tsv`, or any file where the first column has gene symbols. Skips header if first cell is `gene`/`symbol`/case-insensitive. - `--top` limits the dossier to the top N input genes (default 50) — input order is preserved up to that cut, then composite-score re-ranks within. `orchestrate.py` runs the five fetchers in parallel (Python threads, since all calls are I/O-bound). Each writes a self-contained JSON to `/raw_data/.json`. Then `aggregate.py` merges them, computes the composite score using `weights.yaml`, writes `targets_summary.csv`, and emits a `targets_report.md` skeleton with one section per gene — the **rationale and risks fields are left blank for Claude to fill**. ## Composite score Weights live in `weights.yaml` and can be overridden per-run with `--weights`. Defaults aim for "find druggable, genetically supported targets with clean therapeutic window and expression in the cell of interest": ``` composite_score = w1 * druggability_score (approved drugs, tractability, clin trials) + w2 * disease_genetics_score (OpenTargets disease associations + focus-disease bonus) + w3 * tractability_bonus (surface or secreted vs intracellular) + w4 * tissue_specificity (HPA tissue tag — narrow expression = cleaner window) + w5 * cell_context_score (HPA single-cell nCPM rank in FOCUS_CELL_TYPES) + w6 * essentiality_score (DepMap CRISPR % essential, pan-essentials capped) + w7 * safety_constraint_score (gnomAD LOEUF — high = LoF tolerated → safer to inhibit) + w8 * expression_score (from input DE if present) + w9 * novelty_bonus (favors moderately studied) - w10 * over_studied_penalty (PubMed total > cap → diminishing returns) ``` ChEMBL contributes dossier columns (`chembl_target_id`, `chembl_best_pchembl`, `chembl_best_ic50_nm`, `chembl_top_compounds`) but no score component — its job is to surface concrete tool compounds for the "Suggested next step" slot. Each component is normalized to [0, 1]. The composite is therefore roughly in [-w7, sum(w1..w6)] and is min-max rescaled before reporting. **Read `weights.yaml` for the current defaults.** ## Writing the rationale After `aggregate.py` produces `targets_report.md` with blank rationale slots, Claude reads the per-gene dossier rows and writes a 2-3 sentence rationale per gene. Use the template in `prompts/rationale_template.md` — it specifies the structure (one line on the most compelling evidence, one line on the main risk, one line on the suggested next experimental step). For the top 5–10 genes by composite score, also write a short executive summary at the top of the report. Keep it factual and grounded in the dossier data; do not hallucinate beyond what the JSONs contain. ## Data source notes All free, no API key needed. Rate limits handled in fetchers: - **UniProt REST** — 100 req/sec, batched via `accession` query - **OpenTargets GraphQL** — generous, single endpoint; provides disease genetics signal via integrated `associatedDiseases` - **PubMed E-utilities** — 3 req/sec without key; fetchers respect this - **Human Protein Atlas** — `search_download.php` for symbol→ENSG, then per-ENSG `/.json`; no rate limit documented, fetcher sleeps 0.15s/gene - **DepMap CRISPR essentiality** — fetched via `target.depMapEssentiality` inside the OpenTargets call (no separate endpoint) - **gnomAD constraint** — fetched via `target.geneticConstraint` inside the OpenTargets call (avoids gnomAD's WAF on direct API access) - **ChEMBL REST** — `target/search.json` then `activity.json`; ~5 req/sec friendly, fetcher sleeps 0.2s/gene For deeper API details and field mappings, see `references/api_endpoints.md`. ## Retargeting the focus disease + cell context The skill ships with an autoimmunity / T-cell default but is intentionally disease-agnostic. Three edits switch the focus: - `scripts/fetch_opentargets.py` and `scripts/aggregate.py` — change `FOCUS_DISEASE_TERMS` to the lowercased substrings that should mark a drug or disease association as "in-scope" (e.g. `("cancer", "carcinoma", "lymphoma")` for oncology; `("alzheimer", "parkinson", "huntington", "als")` for neurodegeneration; `("diabetes", "obesity", "fatty liver", "nash")` for metabolic disease). - `scripts/aggregate.py` — change `FOCUS_CELL_TYPES` to the HPA single-cell type names that should drive `cell_context_score`. Must match HPA's exact strings (case-sensitive); see comment block above the tuple for examples per domain. - `scripts/fetch_pubmed.py` — adjust the `focus_disease` and `cell_context` queries in `CONTEXTS` (these power the PubMed counts in the dossier). No other code changes are needed; the CSV column names already use the neutral `focus_disease_*` / `cell_context` prefixes. ## When NOT to use this skill - Single-gene look-ups (overkill — just ask Claude to web-search) - Non-human genes (most APIs are human-only; fetchers will silently return empty) - Pure literature review without target ambition — use `scholar-deep-research` or `literature-review` instead ## Iteration tips The pipeline is designed to be re-runnable cheaply: - Raw JSON cache means re-scoring with different `weights.yaml` is a one-second `aggregate.py` rerun - To add a new evidence source, add `scripts/fetch_.py` that writes `raw_data/.json` with the same `{gene: {fields}}` shape, then add a corresponding term in `aggregate.py::compute_composite_score`. --- *Source: https://skills.yangsir.net/skill/gh-target-prioritization* *Markdown mirror: https://skills.yangsir.net/api/skill/gh-target-prioritization/markdown*