ddharmon 0.3.0

Python client for the BioMapper2 API — map biological entities to standardized knowledge graph identifiers

ddharmon

Python client for the BioMapper2 API — map biological entity names to standardized knowledge-graph identifiers (CHEBI, HMDB, PubChem, RefMet, and more).

from ddharmon import map_entity

result = map_entity("L-Histidine")
print(result.primary_curie)     # RM:0129894
print(result.confidence_tier)   # high
print(result.ids_for("CHEBI"))  # ['15971']

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Installation

# Core (async HTTP client + Pydantic models)
pip install ddharmon

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Getting an API key

The BioMapper2 API requires an API key. To request access, email [e-mail].

Once you have a key, set it in your environment:

export BIOMAPPER_API_KEY=your-key-here

Or add it to a .env file in your project root:

BIOMAPPER_API_KEY=your-key-here

ddharmon will pick it up automatically from either location.

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Quick start

Single lookup (synchronous)

from ddharmon import map_entity

result = map_entity("L-Histidine")

print(result.resolved)          # True
print(result.primary_curie)     # RM:0129894
print(result.chosen_kg_id)      # CHEBI:15971
print(result.confidence_score)  # 2.489
print(result.confidence_tier)   # high  (≥2.0)
print(result.ids_for("CHEBI"))  # ['15971']
print(result.ids_for("refmet_id"))  # ['RM0129894']

Batch mapping (synchronous)

from ddharmon import map_entities, summarize

records = [
    {"name": "L-Histidine"},
    {"name": "Glucose", "identifiers": {"HMDB": "HMDB00122"}},
    {"name": "Sphinganine"},
]

results = map_entities(records, progress=True)  # tqdm bar with [notebook]
summary = summarize(results)

print(f"{summary.resolved}/{summary.total_queried} resolved")
print(f"Resolution rate: {summary.resolution_rate:.1%}")
print(summary.vocabulary_coverage)

Inputs are auto-chunked at 1000 entities per request against the native POST /map/batch endpoint, so 10,000 records cost 10 round-trips.

Discovering what the API supports

from ddharmon import list_annotators, list_vocabularies, list_entity_types

for a in list_annotators():
    print(f"{a.slug:30s} {a.name}")

# 300+ supported vocabularies (CHEBI, HMDB, PubChem, …)
vocabs = list_vocabularies()
print(f"{len(vocabs)} vocabularies supported")

# Biolink entity types with their known aliases
for et in list_entity_types():
    print(f"{et.type}: {', '.join(et.aliases)}")

Async usage

import asyncio
from ddharmon import BioMapperClient

async def main() -> None:
    async with BioMapperClient() as client:
        # Verify connectivity
        health = await client.health_check()
        print(health)  # {'status': 'healthy', ...}

        # Single
        result = await client.map_entity(
            "L-Histidine",
            identifiers={"HMDB": "HMDB00177"},
        )

        # Batch — auto-chunked at 1000 entities per request
        results = await client.map_entities(
            [{"name": "L-Histidine"}, {"name": "Glucose"}],
            progress=True,
        )

asyncio.run(main())

Jupyter notebooks

Apply nest_asyncio before using sync helpers inside a running event loop:

import nest_asyncio
nest_asyncio.apply()  # required in Jupyter

from ddharmon import map_entities
results = map_entities([{"name": "L-Histidine"}], progress=True)

Preprocessing functions

from ddharmon.extras.metabolon import clean_compound_name, extract_hmdb_id

# Strip quotes and collision-energy suffixes
clean_compound_name('"1,3-Diphenylguanidine_CE45"')  # '1,3-Diphenylguanidine'
clean_compound_name('"4,6-DIOXOHEPTANOIC ACID"')     # '4,6-DIOXOHEPTANOIC ACID'
clean_compound_name('L-Histidine')                   # 'L-Histidine'  (unchanged)

# Extract HMDB accessions from ms1_compound_name format
extract_hmdb_id('HMDB:HMDB03349-2257 L-Dihydroorotic acid')  # 'HMDB03349'
extract_hmdb_id('HMDB00177')                                  # 'HMDB00177'
extract_hmdb_id(None)                                         # None

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API reference

MappingResult

Attribute	Type	Description
query_name	str	Name submitted to the API
resolved	bool	Whether any identifier was returned
primary_curie	str | None	First CURIE in the response
chosen_kg_id	str | None	Resolver-selected knowledge graph ID
confidence_score	float | None	Highest score across annotators
confidence_tier	str	"high" (≥2.0) / "medium" (1–2) / "low" (<1) / "unknown"
identifiers	dict[str, list[str]]	Vocabulary → IDs, e.g. {"CHEBI": ["15971"]}
hmdb_hint	str | None	HMDB hint passed in the request
error	str | None	Error message if mapping failed

result.ids_for("CHEBI")        # ['15971']
result.ids_for("refmet_id")    # ['RM0129894']
result.ids_for("PUBCHEM.COMPOUND")  # []

Confidence tiers

Score	Tier	Recommended action
≥ 2.0	high	Accept without review
1.0–2.0	medium	Quick sanity check
< 1.0	low	Manual review recommended
None	unknown	No score returned (e.g. HMDB-hint resolved)

Error handling

from ddharmon import (
    BioMapperError,       # base class
    BioMapperAuthError,   # 401/403 — bad API key
    BioMapperRateLimitError,  # 429 — throttled
    BioMapperServerError,     # 5xx
    BioMapperTimeoutError,    # request timeout
    BioMapperConfigError,     # missing API key / bad config
)

try:
    result = map_entity("Glucose")
except BioMapperRateLimitError as e:
    print(f"Throttled. Retry after: {e.retry_after}s")
except BioMapperAuthError:
    print("Check your BIOMAPPER_API_KEY")

In batch mode (map_entities), per-record errors are caught and returned as MappingResult(error=...) rather than aborting the batch.

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Development

git clone https://github.com/trentleslie/ddharmon
cd ddharmon
poetry install --with dev --extras all

make check          # format → lint → type-check → test
make test           # tests only
make coverage       # HTML coverage report

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License

MIT — see LICENSE.

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Related

• BioMapper2 API: https://biomapper.expertintheloop.io