gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/hmdb-database/references/hmdb_data_fields.md

HMDB Data Fields Reference

This document provides detailed information about the data fields available in HMDB metabolite entries.

Metabolite Entry Structure

Each HMDB metabolite entry contains 130+ data fields organized into several categories:

Chemical Data Fields

Identification:

• accession: Primary HMDB ID (e.g., HMDB0000001)
• secondary_accessions: Previous HMDB IDs for merged entries
• name: Primary metabolite name
• synonyms: Alternative names and common names
• chemical_formula: Molecular formula (e.g., C6H12O6)
• average_molecular_weight: Average molecular weight in Daltons
• monoisotopic_molecular_weight: Monoisotopic molecular weight

Structure Representations:

• smiles: Simplified Molecular Input Line Entry System string
• inchi: International Chemical Identifier string
• inchikey: Hashed InChI for fast lookup
• iupac_name: IUPAC systematic name
• traditional_iupac: Traditional IUPAC name

Chemical Properties:

• state: Physical state (solid, liquid, gas)
• charge: Net molecular charge
• logp: Octanol-water partition coefficient (experimental/predicted)
• pka_strongest_acidic: Strongest acidic pKa value
• pka_strongest_basic: Strongest basic pKa value
• polar_surface_area: Topological polar surface area (TPSA)
• refractivity: Molar refractivity
• polarizability: Molecular polarizability
• rotatable_bond_count: Number of rotatable bonds
• acceptor_count: Hydrogen bond acceptor count
• donor_count: Hydrogen bond donor count

Chemical Taxonomy:

• kingdom: Chemical kingdom (e.g., Organic compounds)
• super_class: Chemical superclass
• class: Chemical class
• sub_class: Chemical subclass
• direct_parent: Direct chemical parent
• alternative_parents: Alternative parent classifications
• substituents: Chemical substituents present
• description: Text description of the compound

Biological Data Fields

Metabolite Origins:

• origin: Source of metabolite (endogenous, exogenous, drug metabolite, food component)
• biofluid_locations: Biological fluids where found (blood, urine, saliva, CSF, etc.)
• tissue_locations: Tissues where found (liver, kidney, brain, muscle, etc.)
• cellular_locations: Subcellular locations (cytoplasm, mitochondria, membrane, etc.)

Biospecimen Information:

• biospecimen: Type of biological specimen
• status: Detection status (detected, expected, predicted)
• concentration: Concentration ranges with units
• concentration_references: Citations for concentration data

Normal and Abnormal Concentrations: For each biofluid (blood, urine, saliva, CSF, feces, sweat):

• Normal concentration value and range
• Units (μM, mg/L, etc.)
• Age and gender considerations
• Abnormal concentration indicators
• Clinical significance

Pathway and Enzyme Information

Metabolic Pathways:

• pathways: List of associated metabolic pathways
  • Pathway name
  • SMPDB ID (Small Molecule Pathway Database ID)
  • KEGG pathway ID
  • Pathway category

Enzymatic Reactions:

• protein_associations: Enzymes and transporters
  • Protein name
  • Gene name
  • Uniprot ID
  • GenBank ID
  • Protein type (enzyme, transporter, carrier, etc.)
  • Enzyme reactions
  • Enzyme kinetics (Km values)

Biochemical Context:

• reactions: Biochemical reactions involving the metabolite
• reaction_enzymes: Enzymes catalyzing reactions
• cofactors: Required cofactors
• inhibitors: Known enzyme inhibitors

Disease and Biomarker Associations

Disease Links:

• diseases: Associated diseases and conditions
  • Disease name
  • OMIM ID (Online Mendelian Inheritance in Man)
  • Disease category
  • References and evidence

Biomarker Information:

• biomarker_status: Whether compound is a known biomarker
• biomarker_applications: Clinical applications
• biomarker_for: Diseases or conditions where used as biomarker

Spectroscopic Data

NMR Spectra:

• nmr_spectra: Nuclear Magnetic Resonance data
  • Spectrum type (1D ¹H, ¹³C, 2D COSY, HSQC, etc.)
  • Spectrometer frequency (MHz)
  • Solvent used
  • Temperature
  • pH
  • Peak list with chemical shifts and multiplicities
  • FID (Free Induction Decay) files

Mass Spectrometry:

• ms_spectra: Mass spectrometry data
  • Spectrum type (MS, MS-MS, LC-MS, GC-MS)
  • Ionization mode (positive, negative, neutral)
  • Collision energy
  • Instrument type
  • Peak list (m/z, intensity, annotation)
  • Predicted vs. experimental flag

Chromatography:

• chromatography: Chromatographic properties
  • Retention time
  • Column type
  • Mobile phase
  • Method details

External Database Links

Database Cross-References:

• kegg_id: KEGG Compound ID
• pubchem_compound_id: PubChem CID
• pubchem_substance_id: PubChem SID
• chebi_id: Chemical Entities of Biological Interest ID
• chemspider_id: ChemSpider ID
• drugbank_id: DrugBank accession (if applicable)
• foodb_id: FooDB ID (if food component)
• knapsack_id: KNApSAcK ID
• metacyc_id: MetaCyc ID
• bigg_id: BiGG Model ID
• wikipedia_id: Wikipedia page link
• metlin_id: METLIN ID
• vmh_id: Virtual Metabolic Human ID
• fbonto_id: FlyBase ontology ID

Protein Database Links:

• uniprot_id: UniProt accession for associated proteins
• genbank_id: GenBank ID for associated genes
• pdb_id: Protein Data Bank ID for protein structures

Literature and Evidence

References:

• general_references: General references about the metabolite
  • PubMed ID
  • Reference text
  • Citation
• synthesis_reference: Synthesis methods and references
• protein_references: References for protein associations
• pathway_references: References for pathway involvement

Ontology and Classification

Ontology Terms:

• ontology_terms: Related ontology classifications
  • Term name
  • Ontology source (ChEBI, MeSH, etc.)
  • Term ID
  • Definition

Data Quality and Provenance

Metadata:

• creation_date: Date entry was created
• update_date: Date entry was last updated
• version: HMDB version number
• status: Entry status (detected, expected, predicted)
• evidence: Evidence level for detection/presence

XML Structure Example

When downloading HMDB data in XML format, the structure follows this pattern:

<metabolite>
  <accession>HMDB0000001</accession>
  <name>1-Methylhistidine</name>
  <chemical_formula>C7H11N3O2</chemical_formula>
  <average_molecular_weight>169.1811</average_molecular_weight>
  <monoisotopic_molecular_weight>169.085126436</monoisotopic_molecular_weight>
  <smiles>CN1C=NC(CC(=O)O)=C1</smiles>
  <inchi>InChI=1S/C7H11N3O2/c1-10-4-8-3-5(10)2-7(11)12/h3-4H,2H2,1H3,(H,11,12)</inchi>
  <inchikey>BRMWTNUJHUMWMS-UHFFFAOYSA-N</inchikey>

  <biospecimen_locations>
    <biospecimen>Blood</biospecimen>
    <biospecimen>Urine</biospecimen>
  </biospecimen_locations>

  <pathways>
    <pathway>
      <name>Histidine Metabolism</name>
      <smpdb_id>SMP0000044</smpdb_id>
      <kegg_map_id>map00340</kegg_map_id>
    </pathway>
  </pathways>

  <diseases>
    <disease>
      <name>Carnosinemia</name>
      <omim_id>212200</omim_id>
    </disease>
  </diseases>

  <normal_concentrations>
    <concentration>
      <biospecimen>Blood</biospecimen>
      <concentration_value>3.8</concentration_value>
      <concentration_units>uM</concentration_units>
    </concentration>
  </normal_concentrations>
</metabolite>

Querying Specific Fields

When working with HMDB data programmatically:

For metabolite identification:

• Query by accession, name, synonyms, inchi, smiles

For chemical similarity:

• Use smiles, inchi, inchikey, molecular_weight, chemical_formula

For biomarker discovery:

• Filter by diseases, biomarker_status, normal_concentrations, abnormal_concentrations

For pathway analysis:

• Extract pathways, protein_associations, reactions

For spectral matching:

• Compare against nmr_spectra, ms_spectra peak lists

For cross-database integration:

• Map using external IDs: kegg_id, pubchem_compound_id, chebi_id, etc.

Field Completeness

Not all fields are populated for every metabolite:

• Highly complete fields (>90% of entries): accession, name, chemical_formula, molecular_weight, smiles, inchi
• Moderately complete (50-90%): biospecimen_locations, tissue_locations, pathways
• Variably complete (10-50%): concentration data, disease associations, protein associations
• Sparsely complete (<10%): experimental NMR/MS spectra, detailed kinetic data

Predicted and computational data (e.g., predicted MS spectra, predicted concentrations) supplement experimental data where available.