metabolomics-workbench-database

Metabolomics Workbench Database — REST API Access

Overview

The Metabolomics Workbench (MW) REST API at https://www.metabolomicsworkbench.org/rest/ exposes 4,200+ metabolomics studies hosted at UCSD under NIH Common Fund sponsorship. URL pattern is /{context}/{input_item}/{input_value}/{output_item}/{format}. Contexts include compound, refmet, moverz, study, analysis, metabolite, gene, protein. Notable quirks discovered live:

• compound/name/{x} is rejected — name is not an allowed input_item. Use pubchem_cid, kegg_id, inchi_key, hmdb_id, regno, lm_id, formula, smiles, or abbrev. For free-text input, go through refmet/match/{x} first.
• refmet/name/{x}/all requires the exact RefMet name (e.g. Glucose, not D-glucose); use refmet/match/{x} for fuzzy normalisation first.
• moverz/{REFMET|LIPIDS|MB}/{mz}/{ion}/{tol}/txt returns TSV text (no JSON variant).
• The metstat/filter/... endpoint shown in older examples returns [] — replace with study/{context}/{value}/summary (or /metabolites) + client-side filtering.

No authentication required.

When to Use

• Searching metabolite records by PubChem CID, KEGG ID, InChIKey, HMDB ID, formula, or SMILES
• Discovering studies by species, disease, last_name, institute, analysis_type, or polarity
• Standardising metabolite names to RefMet nomenclature for cross-study integration
• Identifying unknown compounds from MS m/z values with adduct-aware matching (moverz)
• Retrieving experimental metabolite tables (analyses, abundances) from published studies
• Querying gene/protein annotations linked to metabolomics pathways
• Downloading raw mwTab files for local analysis
• For local 220K-metabolite XML parsing with NMR/MS spectra use hmdb-database instead
• For live 110M-compound property lookups use pubchem-compound-search instead

Prerequisites

• Python packages: requests, pandas
• No API key required: publicly accessible
• Rate limits: MW does not enforce strict limits; add time.sleep(0.3) between bulk requests
• Base URL: https://www.metabolomicsworkbench.org/rest

pip install requests pandas

Quick Start

import requests

BASE = "https://www.metabolomicsworkbench.org/rest"

# Two-step free-text → compound (the API rejects compound/name/...)
def lookup_by_name(name):
    # 1) Normalise to RefMet name
    r = requests.get(f"{BASE}/refmet/match/{name}", timeout=30)
    r.raise_for_status()
    refmet = r.json()
    if not refmet.get("refmet_name"):
        return None
    # 2) Pull full compound record by RefMet name (or by pubchem_cid)
    r2 = requests.get(f"{BASE}/refmet/name/{refmet['refmet_name']}/all", timeout=30)
    rec = r2.json() if r2.json() else {}
    return rec if isinstance(rec, dict) else None

c = lookup_by_name("glucose")
print(f"{c['name']}: formula={c['formula']}, PubChem CID={c['pubchem_cid']}, "
      f"InChIKey={c['inchi_key']}")
# Glucose: formula=C6H12O6, PubChem CID=5793, InChIKey=WQZGKKKJIJFFOK-GASJEMHNSA-N

Core API

Module 1: Compound Queries

compound/{input_item}/{input_value}/all/json — input_item must be one of regno, formula, inchi_key, lm_id, pubchem_cid, hmdb_id, kegg_id, smiles, abbrev. The legacy name input is rejected by the server.

import requests

BASE = "https://www.metabolomicsworkbench.org/rest"

# By PubChem CID
r = requests.get(f"{BASE}/compound/pubchem_cid/5793/all/json", timeout=30)
glucose = r.json()
print(f"PubChem 5793 -> {glucose['name']}, formula={glucose['formula']}, "
      f"HMDB={glucose.get('hmdb_id')}, KEGG={glucose.get('kegg_id')}")

# By KEGG ID
r = requests.get(f"{BASE}/compound/kegg_id/C00031/all/json", timeout=30)
print("KEGG C00031 ->", r.json()["name"])

# By InChIKey
r = requests.get(f"{BASE}/compound/inchi_key/WQZGKKKJIJFFOK-GASJEMHNSA-N/all/json", timeout=30)
print("InChIKey -> regno:", r.json()["regno"])

# Compound by formula returns a paged dict (multiple matches)
import requests
BASE = "https://www.metabolomicsworkbench.org/rest"
r = requests.get(f"{BASE}/compound/formula/C6H12O6/all/json", timeout=30)
matches = r.json()
print(f"Compounds with formula C6H12O6: {len(matches)}")
for k in list(matches)[:3]:
    print(f"  regno={matches[k]['regno']}  name={matches[k]['name']}")

Module 2: Study Discovery

study/{input_item}/{input_value}/{output} — input_item includes study_id, study_title, last_name, institute, analysis_id, metabolite_id, kegg_id, refmet_name. output includes summary, metabolites, factors, data, available_studies, species, disease. summary for study_id returns a dict (keyed by accession when multiple); for last_name/institute it returns a list.

import requests, pandas as pd

BASE = "https://www.metabolomicsworkbench.org/rest"

# Single-study summary — `study/study_id/{id}/summary` returns a flat dict
# (keys: study_id, study_title, species, institute, analysis_type, ...)
r = requests.get(f"{BASE}/study/study_id/ST000001/summary", timeout=30)
s = r.json()
print(f"{s['study_id']}: {s['study_title'][:60]}")
print(f"  Species : {s.get('species')}  Institute: {s.get('institute')}")
print(f"  Submit  : {s.get('submission_date')}")

# Studies that detected a metabolite — `study/refmet_name/{x}/summary` returns
# a thin index of (refmet_name, kegg_id, study_id) rows. Chain study_id → full summary
# to get title and species.
import requests, pandas as pd
BASE = "https://www.metabolomicsworkbench.org/rest"
r = requests.get(f"{BASE}/study/refmet_name/Glucose/summary", timeout=60)
d = r.json()
rows = list(d.values()) if isinstance(d, dict) else d
print(f"Studies referencing 'Glucose': {len(rows)}")
print(pd.DataFrame(rows).head(5).to_string(index=False))
# refmet_name kegg_id  study_id
#     Glucose  C00031  ST000001
#     Glucose  C00031  ST000002
#     ...

Module 3: RefMet Standardisation

refmet/match/{user_text} is a fuzzy normaliser — returns the standard RefMet record (no pubchem_cid/kegg_id though). refmet/name/{exact_refmet_name}/all returns the full record including IDs. Use them as a two-step pipeline.

import requests

BASE = "https://www.metabolomicsworkbench.org/rest"

def normalise_to_refmet(user_text):
    r = requests.get(f"{BASE}/refmet/match/{user_text}", timeout=30)
    r.raise_for_status()
    m = r.json()
    if not m or not m.get("refmet_name"):
        return None
    return m["refmet_name"]

def refmet_full(refmet_name):
    r = requests.get(f"{BASE}/refmet/name/{refmet_name}/all", timeout=30)
    r.raise_for_status()
    rec = r.json()
    return rec if isinstance(rec, dict) and rec else None

name = normalise_to_refmet("alpha-D-glucose")  # -> 'Glucose'
print(f"Normalised: {name}")
rec = refmet_full(name)
print(f"  PubChem CID : {rec['pubchem_cid']}")
print(f"  InChIKey    : {rec['inchi_key']}")
print(f"  Super class : {rec['super_class']} / {rec['main_class']} / {rec['sub_class']}")

Module 4: Study Filtering (replaces broken metstat)

The older metstat/filter/... endpoint returns []. Use the study context endpoints with client-side filtering instead.

import requests, pandas as pd

BASE = "https://www.metabolomicsworkbench.org/rest"

def study_ids_for_metabolite(refmet_name):
    """Return the study_id list that report a given RefMet name."""
    r = requests.get(f"{BASE}/study/refmet_name/{refmet_name}/summary", timeout=60)
    r.raise_for_status()
    d = r.json()
    rows = list(d.values()) if isinstance(d, dict) else d
    return sorted({row["study_id"] for row in rows if row.get("study_id")})

def study_summary(study_id):
    """Pull full summary (title, species, institute, dates) for one study_id.
    Response is a flat dict with keys study_id/study_title/species/institute/..."""
    return requests.get(f"{BASE}/study/study_id/{study_id}/summary", timeout=30).json()

# Find glucose s