keggtangled 0.6.0

A KEGG tool for managing compounds, reactions, and pathways

🧬 KEGGtangled

Tie all your KEGG data together. Untangle it your way.

keggtangled is a Python library that lets you download, cache, and interrogate the entire metabolic network of any organism in the KEGG database.
It builds a rich, in‑memory knowledge graph connecting genes → KOs → reactions → compounds, so you can answer biological questions with clean, Pythonic code.

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📦 Installation

pip install keggtangled

keggtangled requires Python 3.8 or later and depends on biopython.
Optionally, install tqdm for progress bars during bulk downloads:

pip install tqdm

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🚀 Quick start

import keggtangled as kt

# Create an organism – first run fetches and caches all KO‑gene links and reactions
mta = kt.Organism("mta", cache_dir="kegg_cache")

# Load a pathway (downloads + parses KGML, caches everything)
glycolysis = mta.load_pathway("mta00010")
print(glycolysis)
# Pathway(mta00010, 44 genes, 23 reactions)

# From gene → KOs → reactions → compounds
gene = "Moth_0033"
kos = mta.get_kos_for_gene(gene)            # frozenset of ko:K...
rxns = mta.get_reactions_for_gene(gene)     # frozenset of reaction IDs

# Get a specific reaction and its readable formula
rxn = mta.get_reaction("R00200")
print(rxn.formula_per_pathway["mta00010"]["formula_read"])
# e.g., "ATP + D-Glucose --> ADP + D-Glucose 6-phosphate"

# Work with a compound
atp = mta.get_compound("C00074")
print(f"{atp.name} – {atp.formula} – mass {atp.mass}")
# ATP – C10H16N5O13P3 – mass 507.181

# Which genes are linked to ATP?
print(atp.get_genes())    # frozenset of locus tags

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🧠 What can you do with KEGGtangled?

• Fetch & cache all KEGG data for any organism code (e.g., eco, hsa, mta) with a single line.
  No more manual downloads or API wrangling.

• Trace metabolic connections effortlessly:

  • Gene → KOs → Reactions → Compounds (and back)
  • Built‑in walkers: get_reactions_for_gene(), get_compounds_for_reaction(), get_genes_for_reaction()

• Inspect pathways in detail:

  • Readable reaction formulas (substrate / product names)
  • Reversible vs. irreversible arrows (<=> / -->)
  • Gene lists per pathway, pathways per gene, etc.

• Save & resume your work:

  • organism.save("my_eco.pkl") snapshots the fully‑loaded state
  • Organism.load("my_eco.pkl") restores it instantly – no network calls, no recomputation

• Iterate over everything:

  for comp in mta.compounds:
      print(comp.id, comp.name)
  for rxn in mta.all_reactions:
      print(rxn.reaction_id)
  

• Get a quick overview with mta.summary() or just print(mta).

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🔍 Key API at a glance

Method / Property	Description
Organism(org_code, cache_dir)	Create or load a KEGG organism
.load_pathway(pathway_id)	Load a pathway (genes, reactions, compounds, formulas)
.load_all_cached_pathways()	Load every pathway already cached on disk
.get_compound("C00022")	Retrieve a compound (fetches details if needed)
.get_reaction("R00200")	Retrieve a reaction
.get_genes_for_ko(ko)	All genes annotated with a KO
.get_reactions_for_ko(ko)	All reactions linked to a KO
.get_kos_for_gene(locus_tag)	KOs linked to a gene
.get_reactions_for_gene(locus_tag)	Reactions linked to a gene (via KOs)
.get_compounds_for_reaction(rxn_id)	Compound IDs involved in a reaction
.get_genes_for_reaction(rxn_id)	Genes linked to a reaction
.get_pathways_for_gene(locus_tag)	Pathways containing a gene
.get_pathways_for_reaction(rxn_id)	Pathways containing a reaction
.save(filename)	Pickle the entire organism with version check
Organism.load(filename)	Load a previously saved organism
.compounds / .all_reactions / .all_pathways	Iterate over loaded data
.summary()	Dictionary with counts of pathways, reactions, compounds, genes, KOs

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📂 Caching & performance

keggtangled stores everything it downloads in the cache_dir folder:

• KO‑gene and KO‑reaction mappings (JSON)
• Pathway flat files and parsed KGML (both raw XML and pickled parsed objects)
• Compound details (name, formula, mass)

After the first run, subsequent Organism() creations will load from cache in seconds.
When you save an organism with .save(), all in‑memory computed relations are preserved, so loading is nearly instant.

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🧪 Example: discover all compounds linked to a gene

# Find every compound that 'Moth_0033' touches
reactions = mta.get_reactions_for_gene("Moth_0033")
all_compounds = set()
for rxn in reactions:
    all_compounds.update(mta.get_compounds_for_reaction(rxn))

for cid in sorted(all_compounds):
    comp = mta.get_compound(cid)
    print(f"{comp.id:10s} {comp.name or 'unknown'}")

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📚 Requirements & dependencies

• Python ≥ 3.8
• Biopython (for KEGG API access and KGML parsing)
• Optional: tqdm – shows progress bars during large fetches.

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🤝 Contributing

Bug reports, feature requests, and pull requests are welcome!
Please check the issues page first.

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📄 License

keggtangled is released under the MIT License.
KEGG data is provided by Kanehisa Laboratories – please see their terms of use.