large-scale metabolic network alignment: metacyc and kegg

1 SRI International Bioinformatics

Large-Scale Metabolic Network Alignment: MetaCyc and KEGG

Tomer AltmanBioinformatics Research Group

SRI International

[email protected]


Problem Motivation

There are an increasing number of ‘encyclopedic’ metabolic networks, or reaction databases

KEGG and MetaCyc, plus Rhea, BRENDA, and GO A natural question to ask is, “what is similar /

different between them?”There has been some linking of MetaCyc

compounds to KEGG, but none for reactions


Challenges with Mapping Objects

Multiple aspects to compare (name, chemical structure, reaction substrates, external identifiers)

Inexact namingInexact structures (different specificity of

stereocenters)Inexact description of reactions (classes vs.

instances, proton-balancing)How to combine the evidence in a logical fashion


Evidence / Prediction Data Structure

Prediction typePredictor nameMetaCyc Object Frame IDKEGG Object identifierIteration numberParameters used


Compound Evidence

Curated MetaCyc links to KEGGName matchingPubChem identifier mapping (used for ChEBI as

well)Molecular Fingerprint Tanimoto Similarity

CoefficientInChI string comparisonExact Sub-Structure Match (no stereochemistry)‘All-but-one’ inference


Compound Prediction Detail: ‘All-but-one’

Most of the compounds between these two reactions are the sameClass vs. instance, and naming issues lead to unknown match between “acceptor” and “oxidized electron acceptor”


Reaction Evidence

EC NumbersUniProt Accession NumbersName matches (gleaned from associated objects)Exact equation matchInexact equation match (cosine similarity)


Reaction Prediction Detail: UniProt Mapping

Use UniProt Accession numbers to map the enzymes in MetaCyc and KEGG to one anotherUse UniRef 90 or 100 to map “the same protein” when not exact same Accession Number


From Evidence to Prediction

Rule #1: Evidence is partitioned into exact and inexact quality types

Rule #2: Evidence is partitioned (orthogonally) into qualitative and quantitative types

Rule #3: A high-quality prediction will consist of qualitative and quantitative evidence in favor

Rule #4: We try exact quality types first, then inexact Rule #5: No contradictory predictions Rule #6: Iterate until no new predictions This is the general idea, but implementation has some

domain-based heuristics thrown in, such as with EC numbers


Statistics

3269 MetaCyc reactions with links to KEGG (~75%)

4004 MetaCyc compounds with links to KEGG (>50%)


Future Work

Greatest Common Subgraph Compound Matching Sensitivity / Specificity characterization of all evidence types Analysis of unmatched content of KEGG and MetaCyc for

algorithm improvement and focused curation (inexact reaction matching and novel reaction import)

Hierarchical clustering of compounds and reactions A general tool that can be used for any two given metabolic

networks Recasting algorithm in a machine learning framework


Acknowledgements

•Peter Karp•Douglas Brutlag•Dan Davison•Anamika Kothari•Joseph Dale

MetaCyc.org


… One more thing!

Pathway Tools API (beta) http://www.ai.sri.com/~taltman/ptools-api/ptools_api.html

large-scale metabolic network alignment: metacyc and kegg

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