OREGANO Query Skill
Search the OREGANO knowledge graph (88,937 nodes, 824,231 links) by any entity. Auto-resolves input to OREGANO node IDs via cross-reference tables.
| Input Pattern |
Detected As |
Match Logic |
OREGANO internal ID (e.g. 1234) |
OREGANO node ID |
exact in triplet index |
DB00331 / DrugBank ID |
external xref |
exact in COMPOUND.tsv |
| UniProt / KEGG / MeSH / UMLS ID |
external xref |
exact across all metadata TSVs |
metformin, BRCA1, free text |
entity name |
substring on name columns |
API
| Function |
Input |
Returns |
search(query) |
single entity string |
dict: {query, resolved_ids, metadata, triplets} |
search_batch(queries) |
list of entity strings |
dict[str, search_result] |
summarize(result) |
search result dict |
compact LLM-readable text |
to_json(result) |
search result dict |
JSON-serializable dict |
get_stats() |
— |
graph-level counts (triplets, nodes, predicates, entity types) |
Graph Schema
11 node types: Compound (90,868), Gene (35,794), Target (22,096), Disease (18,333), Phenotype (11,605), Side Effect (6,060), Indication (2,714), Pathway (2,129), Effect (171), Activity (78).
19 relation types (predicates): e.g. has_target, has_indication, has_side_effect, interacts_with, involved_in_pathway, associated_with, has_phenotype, etc. Run get_stats() to list all predicates with counts.
Usage
See if __name__ == "__main__" block in 21_OREGANO.py for runnable examples covering: free-text drug name search, DrugBank ID lookup, batch search, JSON pipeline output, and graph statistics.
Data
- Source: Zenodo DOI 10.5281/zenodo.10103842 (CC-BY 4.0)
- Version: v2.1 (published 2023-11-10)
- Core file:
OREGANO_V2.1.tsv — tab-delimited triplets (Subject, Predicate, Object)
- Metadata files:
COMPOUND.tsv, TARGET.tsv, GENES.tsv, DISEASES.tsv, PHENOTYPES.tsv, PATHWAYS.tsv, INDICATION.tsv, SIDE_EFFECT.tsv, ACTIVITY.tsv, EFFECT.tsv
- Path:
DATA_DIR variable in 21_OREGANO.py
Citation
Boudin, M., Diallo, G., Drancé, M. & Mougin, F. The OREGANO knowledge graph for computational drug repurposing. Sci Data 10, 871 (2023). https://doi.org/10.1038/s41597-023-02757-0
1---2name: oregano-query3description: Query the OREGANO knowledge graph for computational drug repurposing. Use whenever the user asks about drug–target–disease–gene–pathway relationships, compound cross-references, drug repurposing hypotheses, or wants to explore neighbors of any biomedical entity in a knowledge graph that includes natural compounds.4---5
6# OREGANO Query Skill
7
8Search the OREGANO knowledge graph (88,937 nodes, 824,231 links) by any entity. Auto-resolves input to OREGANO node IDs via cross-reference tables.
9
10| Input Pattern | Detected As | Match Logic |
11|---|---|---|
12| OREGANO internal ID (e.g. `1234`) | OREGANO node ID | exact in triplet index |
13| `DB00331` / DrugBank ID | external xref | exact in COMPOUND.tsv |
14| UniProt / KEGG / MeSH / UMLS ID | external xref | exact across all metadata TSVs |
15| `metformin`, `BRCA1`, free text | entity name | substring on name columns |
16
17## API
18
19| Function | Input | Returns |
20|---|---|---|
21| `search(query)` | single entity string | dict: `{query, resolved_ids, metadata, triplets}` |
22| `search_batch(queries)` | list of entity strings | `dict[str, search_result]` |
23| `summarize(result)` | search result dict | compact LLM-readable text |
24| `to_json(result)` | search result dict | JSON-serializable dict |
25| `get_stats()` | — | graph-level counts (triplets, nodes, predicates, entity types) |
26
27## Graph Schema
28
29**11 node types**: Compound (90,868), Gene (35,794), Target (22,096), Disease (18,333), Phenotype (11,605), Side Effect (6,060), Indication (2,714), Pathway (2,129), Effect (171), Activity (78).
30
31**19 relation types** (predicates): e.g. `has_target`, `has_indication`, `has_side_effect`, `interacts_with`, `involved_in_pathway`, `associated_with`, `has_phenotype`, etc. Run `get_stats()` to list all predicates with counts.
32
33## Usage
34
35See `if __name__ == "__main__"` block in `21_OREGANO.py` for runnable examples covering: free-text drug name search, DrugBank ID lookup, batch search, JSON pipeline output, and graph statistics.
36
37## Data
38
39- **Source**: Zenodo DOI 10.5281/zenodo.10103842 (CC-BY 4.0)
40- **Version**: v2.1 (published 2023-11-10)
41- **Core file**: `OREGANO_V2.1.tsv` — tab-delimited triplets (Subject, Predicate, Object)
42- **Metadata files**: `COMPOUND.tsv`, `TARGET.tsv`, `GENES.tsv`, `DISEASES.tsv`, `PHENOTYPES.tsv`, `PATHWAYS.tsv`, `INDICATION.tsv`, `SIDE_EFFECT.tsv`, `ACTIVITY.tsv`, `EFFECT.tsv`
43- **Path**: `DATA_DIR` variable in `21_OREGANO.py`
44
45## Citation
46
47Boudin, M., Diallo, G., Drancé, M. & Mougin, F. The OREGANO knowledge graph for computational drug repurposing. *Sci Data* 10, 871 (2023). https://doi.org/10.1038/s41597-023-02757-0
gabrielmoreira