Immutable. This exact content is served forever at /api/v1/blob/da10eb786ba49277.
---
name: target-prioritization
description: Prioritize drug targets from a ranked gene list (e.g., scRNA-seq DE output) by orchestrating parallel API queries against UniProt, OpenTargets, and PubMed, then re-ranking by a composite score combining protein localization, druggability, disease genetics, and research maturity. Use whenever the user wants to filter, triage, prioritize, or "do due diligence" on a list of candidate genes for drug discovery, especially after a DE / DEG analysis when they say things like "which of these should I follow up on", "filter for druggable targets", "make a target dossier", "rank these for tractability", "annotate these genes for druggability", or "build a target report". Trigger even when the user says just "filter these candidate genes" or hands over a CSV from a DE pipeline.
metadata: {"openclaw":{"requires":{"bins":["python3","curl"]},"emoji":"🎯"},"version":"0.2.0"}
---
# 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 `<output_dir>/`:
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/<source>.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)
└─► fetch_pubmed.py → paper counts (total + focus_disease + cell_context)
│
▼
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 <gene_list.csv_or_txt> \
--output <output_dir> \
[--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 three fetchers in parallel (Python threads, since
all calls are I/O-bound). Each writes a self-contained JSON to
`<output_dir>/raw_data/<source>.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 known
biology":
```
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 * expression_score (from input DE if present)
+ w5 * novelty_bonus (favors moderately studied)
- w6 * over_studied_penalty (PubMed total > cap → diminishing returns)
```
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
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. Two 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/fetch_pubmed.py` — adjust the `focus_disease` and
`cell_context` queries in `CONTEXTS` (e.g.
`"hepatocyte"`, `"neuron"`, `"macrophage"` instead of `"T cell"`).
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_<source>.py` that writes
`raw_data/<source>.json` with the same `{gene: {fields}}` shape, then add
a corresponding term in `aggregate.py::compute_composite_score`.