biomapper 1.2.0

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

biomapper

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

from biomapper 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']
print(result.equivalent_ids_for("HMDB"))  # ['HMDB0000177']

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Installation

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

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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

biomapper will pick it up automatically from either location.

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Quick start

Single lookup (synchronous)

from biomapper 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']

# KG equivalent IDs — all identifiers from the resolved knowledge graph node
print(result.kg_equivalent_ids)            # {'CHEBI': ['15971', '44637'], 'HMDB': ['HMDB0000177'], ...}
print(result.equivalent_ids_for("HMDB"))   # ['HMDB0000177']

Batch mapping (synchronous)

from biomapper 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.

Dataset upload (synchronous)

For larger inputs, hand the server a TSV/CSV file directly and stream results back. The server processes the file row-by-row over the POST /map/dataset/stream endpoint:

from pathlib import Path
from biomapper import map_dataset_file_sync

result = map_dataset_file_sync(
    Path("compounds.tsv"),
    name_column="name",
    provided_id_columns=["hmdb_id"],
    progress=True,         # tqdm bar
    total_hint=1000,       # optional; enables % progress
)
result.raise_for_error()   # opt-in: raise BioMapperError if the stream truncated
print(f"resolved {sum(1 for r in result.results if r.resolved)} of {len(result.results)}")

name_column and provided_id_columns are required — the server uses them to map your file's columns to entity names and identifier hints. For per-result streaming into a UI or custom processing, use the async BioMapperClient.map_dataset_file_iter method (see the tutorial notebook in notebooks/).

Discovering what the API supports

from biomapper 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 aliases and default vocabulary prefixes
for et in list_entity_types():
    print(f"{et.type}: {', '.join(et.aliases)}")
    if et.default_prefixes:
        print(f"  prefixes: {', '.join(et.default_prefixes)}")

Async usage

import asyncio
from biomapper 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,
        )

        # Stream from a file — per-result as they arrive
        from pathlib import Path
        async for r in client.map_dataset_file_iter(
            Path("compounds.tsv"),
            name_column="name",
            provided_id_columns=["hmdb_id"],
        ):
            print(r.query_name, r.primary_curie)

asyncio.run(main())

map_dataset_file_iter is the primitive for UIs and custom processing that want per-result reactivity. Callers needing a blocking, fully-collected result should use map_dataset_file_sync instead (see above).

Jupyter notebooks

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

import nest_asyncio
nest_asyncio.apply()  # required in Jupyter

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

Preprocessing functions

from biomapper.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"]}
kg_equivalent_ids	dict[str, list[str]]	All equivalent IDs from the resolved KG node, by CURIE prefix
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")  # []

# KG equivalent IDs — all identifiers from the resolved knowledge graph node
result.equivalent_ids_for("HMDB")  # ['HMDB0000177']
result.equivalent_ids_for("LM")   # ['ST01010001', 'ST01010093']

DatasetMappingResult

Return type of map_dataset_file_sync. Captures per-row results plus an opt-in error signal for partial runs.

Attribute	Type	Description
results	list[MappingResult]	Per-row mapping outcomes in server-emitted order
stats	dict[str, Any]	Server-provided summary. Empty unless the stream emits a terminal summary line
metadata	ApiMetadata	Request metadata; stays at defaults when the stream truncates before completion
error	str | None	Mid-stream transport failure text. None on clean runs

result.raise_for_error()   # raises BioMapperError if .error is set; else no-op

raise_for_error mirrors httpx.Response.raise_for_status and turns the partial-result contract into an explicit caller opt-in — silent consumption of a truncated run (using .results without checking .error) is the footgun this model is designed to prevent.

Note: confidence_score on dataset-stream results is always None — the /map/dataset/stream endpoint emits a slimmer per-row payload than /map/batch and does not include the annotator assigned_ids block. Use map_entity / map_entities if you need confidence tiers.

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 biomapper 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.

Dataset streaming (map_dataset_file_sync) uses a two-tier contract:

• Initial-request errors (401, 422, 500, connect timeout) raise as typed exceptions — these happen before any row is processed, so partial results don't exist to preserve.
• Mid-stream transport failures are captured into DatasetMappingResult.error with the partial results preserved in .results. Call .raise_for_error() to get exception semantics, or inspect .error directly for "accept partial, log the rest" workflows.

Callback exceptions raised from on_result propagate unwrapped and replace the return value — partial results collected up to that point are lost. For UI consumers with failure-prone callbacks, wrap the callback body in your own try/except if you want partial data to survive.

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Development

git clone https://github.com/trentleslie/biomapper
cd biomapper
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