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Challenges in Creating and Curating Plant PGDBs: Lessons Learned from AraCyc and PoplarCyc. Peifen Zhang Carnegie Institution For Science Department of Plant Biology Stanford, CA. Who We Are. PMN: - Sue Rhee ( PI ) - Kate Dreher ( curator ) - A. Karthik ( curator, previous )

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Challenges in Creating and Curating Plant PGDBs: Lessons Learned from AraCyc and PoplarCyc


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  1. Challenges in Creating and Curating Plant PGDBs: Lessons Learned from AraCyc and PoplarCyc Peifen Zhang Carnegie Institution For Science Department of Plant Biology Stanford, CA

  2. Who We Are PMN: - Sue Rhee (PI) - Kate Dreher (curator) - A. Karthik (curator, previous) - Lee Chae (Postdoc) - Anjo Chi (programmer) - Cynthia Lee (TAIR tech team) - Larry Ploetz (TAIR tech team) - Shanker Singh (TAIR tech team) - Bob Muller (TAIR tech team) Key Collaborators: - Peter Karp (MetaCyc, SRI) - Ron Caspi (MetaCYc, SRI) - Lukas Mueller (SGN) - Anuradha Pujar (SGN) http://plantcyc.org

  3. Introduction • Background and rationale • Plants (food, feed, forest, medicine, biofuel…) • An ocean of sequences • More than 60 species in genome sequencing projects, hundreds in EST projects • Putting individual genes onto a network of metabolic reactions and pathways • Annotating, visualizing and analyzing at system level • AraCyc (Arabidopsis thaliana, TAIR/PMN) • predicted by using the Pathway Tools software, followed by manual curation

  4. Introduction (cont) • Background and rationale • Plants (food, feed, forest, medicine, biofuel…) • An ocean of sequences • More than 60 species in genome sequencing projects, hundreds in EST projects • Putting individual genes onto a network of metabolic reactions and pathways • Annotating, visualizing and analyzing at system level • AraCyc (Arabidopsis thaliana, TAIR/PMN) • predicted by using the Pathway Tools software, followed by manual curation • Other plant pathway databases predicted by using the Pathway Tools • RiceCyc (Oryza sativa, Gramene) • MedicCyc (Medicago truncatula, Noble Foundation) • LycoCyc (Solanum lycopersicum, SGN), …

  5. Limitations • Creating pathway databases includes three major components, and is resource-intensive • Sequence annotation • Reference pathway database • Pathway prediction, validation, refinement • Heterogeneous sequence annotation protocols and varying levels of pathway validation impact quality and hinder meaningful cross-species comparison • Using a non-plant reference database causes many false-positive and false-negative pathway predictions

  6. Introducing the PMN • Scope • A platform for plant metabolic pathway database creation • A community for data curation • Curators, editorial board, ally in other databases, researchers • Major goals • Create a plant-specific reference pathway database (PlantCyc) • Create an enzyme sequence annotation pipeline • Enhance pathway prediction by using PlantCyc, and including an automated initial validation step • Create metabolic pathway databases for plant species • e.g. PoplarCyc (Populus trichocarpa), SoyCyc (soybean)

  7. PlantCyc Creation • Nature of PlantCyc • Multiple-species, plants-only • curator-reviewed pathways, predicted, hypothetical, empirical • primary and secondary metabolism • Major Source • All AraCyc pathways and enzymes • Plant pathways and enzymes from MetaCyc • Additional pathways and enzymes manually curated and added • Enzymes from RiceCyc, LycoCyc and MedicCyc

  8. PMN Database Content Statistics PlantCyc 4.0 AraCyc 7.0 PoplarCyc 2.0 Pathways 685 369 288 Enzymes 11058 5506 3420 Reactions 2929 2418 1707 Compounds 2966 2719 1397 Organisms 343 1 1* • Valuable plant natural products, many are specialized metabolites that are limited to a few species or genus. • medicinal: e.g. artemisinin and quinine (treatment of malaria), • codeine and morphine (pain-killer), • ginsenosides (cardio-protectant), • lupenol (antiinflammatory), • taxol and vinblastine (anti-cancer) • industrial materials: e.g. resin and rubber • food flavor and scents: e.g. capsaicin and piperine (chili and pepper flavor), geranyl acetate (aroma of rose) and menthol (mint).

  9. Enzyme Sequence Annotation (version 1.0) • Reference sequences, enzymes with known functions • 14,187 enzyme sequences compiled from GOA-UniProt, Brenda, MetaCyc, and TAIR • 3805 functional identifiers (full EC number, MetaCyc reaction id, GO id) • Annotation methods • BLASTP • Cut-off • unique e-value threshold for each functional identifier

  10. *Accurate: the annotation came from a top hit that has good homology to a known enzyme

  11. Conclusion • Increased performance with potentially true enzymes • Over-prediction for non-enzyme proteins

  12. Enzyme Sequence Annotation (version 2.0, in progress) • Reference sequences, proteins with known functions (ERL) • SwissProt • 117,000 proteins, 26,000 enzymes, 2,400 full EC numbers • Additional enzymes from Brenda, MetaCyc, and TAIR • Functional identifiers: full EC number, MetaCyc reaction id, GO id, • Annotation methods • BLASTP • Priam (enzyme-specific, motif-based) • CatFam (enzyme-specific, motif-based) • Function calling • Ensemble and voting

  13. Enzyme Sequence Annotation (version 2.0, in progress) Lee Chae (unpublished)

  14. Application to the Poplar Genome • Sequence annotation version 1.0 • Pathway Tools version 12.5 • PGDB creation using PlantCyc vs MetaCyc

  15. Comparison of the PoplarCyc Initial Builds with Either PlantCyc or MetaCyc as the Reference Database.

  16. Conclusion • The absolute number of false-positive pathways was reduced significantly by using PlantCyc as the reference • The number of false-negative pathways was comparable using either PlantCyc or MetaCyc as the reference, indicating the usefulness of both databases as references

  17. Automated Initial Pathway Validation • Remove non-plant pathways, identified from manual validation of AraCyc and PoplarCyc • A list of 132 MetaCyc pathways (an up-to-date file is posted online) • Add universal plant pathways • A list of 115 pathways (an up-to-date file is posted online)

  18. A Recap of the PMN Workflow Pathway prediction (PlantCyc) Enzyme sequence annotation Automated pathway validation Pathway prediction (MetaCyc) Manual validation

  19. An Example of Practical Issues

  20. Updating AraCyc with TAIR Functional Annotations • Source and quality • Literature-based GO annotations • Catalytic activities • Experimental evidence (IDA, IMP, IGI, IPI, IEP)

  21. Problem • TAIR: AT5G13700 (polyamine oxidase, IDA, PubMed 16778015) • Polyamine oxidase reactions in MetaCyc/PlantCyc: • Which one of the reaction catalyzed by AT5G13700 was supported in the paper?

  22. Conclusion • Not to automatically propagate GO-exp annotations to enzrxns • Manually enter along with appropriate evidence

  23. Future Work • Enhance pathway prediction and validation • Using additional evidence, such as presence of compounds, weighted confidence of enzyme annotations • Refine pathways, hole-filling • Including non-sequence homology based information in enzyme function prediction, such as phylogenetic profiles, co-expression, protein structure • Create new pathway databases • moss (P. patens), Selaginella, maize, cassava, wine grape … • Add new data types, critical for strategic planning of metabolic engineering • Rate-limiting step • Transcriptional regulator

  24. Thank you for your attention!