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Large-Scale Metabolic Network Alignment: MetaCyc and KEGG

Large-Scale Metabolic Network Alignment: MetaCyc and KEGG. Tomer Altman Bioinformatics Research Group SRI International taltman@ai.sri.com. Problem Motivation. There are an increasing number of ‘encyclopedic’ metabolic networks, or reaction databases

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Large-Scale Metabolic Network Alignment: MetaCyc and KEGG

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  1. Large-Scale Metabolic Network Alignment: MetaCyc and KEGG Tomer Altman Bioinformatics Research Group SRI International taltman@ai.sri.com

  2. 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

  3. Challenges with Mapping Objects • Multiple aspects to compare (name, chemical structure, reaction substrates, external identifiers) • Inexact naming • Inexact structures (different specificity of stereocenters) • Inexact description of reactions (classes vs. instances, proton-balancing) • How to combine the evidence in a logical fashion

  4. Evidence / Prediction Data Structure • Prediction type • Predictor name • MetaCyc Object Frame ID • KEGG Object identifier • Iteration number • Parameters used

  5. Compound Evidence • Curated MetaCyc links to KEGG • Name matching • PubChem identifier mapping (used for ChEBI as well) • Molecular Fingerprint Tanimoto Similarity Coefficient • InChI string comparison • Exact Sub-Structure Match (no stereochemistry) • ‘All-but-one’ inference

  6. Compound Prediction Detail: ‘All-but-one’ • Most of the compounds between these two reactions are the same • Class vs. instance, and naming issues lead to unknown match between “acceptor” and “oxidized electron acceptor”

  7. Reaction Evidence • EC Numbers • UniProt Accession Numbers • Name matches (gleaned from associated objects) • Exact equation match • Inexact equation match (cosine similarity)

  8. Reaction Prediction Detail: UniProt Mapping • Use UniProt Accession numbers to map the enzymes in MetaCyc and KEGG to one another • Use UniRef 90 or 100 to map “the same protein” when not exact same Accession Number

  9. 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

  10. Statistics • 3269 MetaCyc reactions with links to KEGG (~75%) • 4004 MetaCyc compounds with links to KEGG (>50%)

  11. 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

  12. Peter Karp Douglas Brutlag Dan Davison Anamika Kothari Joseph Dale Acknowledgements MetaCyc.org

  13. … One more thing! • Pathway Tools API (beta) • http://www.ai.sri.com/~taltman/ptools-api/ptools_api.html

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