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>/

targets_report.md
— one section per gene, sorted by composite score, with a short LLM-written rationale and recommended next step
targets_summary.csv
— flat table for sorting/filtering in Excel/pandas
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), 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 <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 five 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 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
```
/<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_<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

target-prioritization

NPX Install

Tags

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