Go pathway-interaction-integration
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The document discusses integrating genomic databases like the Gene Ontology (GO) with systems biology databases to enhance analyses and reduce workload. It proposes automating the integration using cross-product definitions to map terms between GO and pathway databases like Reactome. Initial evaluations found many manually assigned mappings were recapitulated and some inferred mappings were better than manual ones. The automated integration improved enrichment results, demonstrating its potential to make analyses more efficient by dividing labor while preventing data silos. Future work includes making the integration tool production-ready and reconciling inconsistencies between databases.
![Automating integration using cross-product definitions – pathway databases[Term]id: GO:0015871name: choline transportintersection_of: GO:0006810 ! transportintersection_of:results_in_transport_ofCHEBI:15354 ! choline](https://image.slidesharecdn.com/go-pathway-interaction-integration-100512225402-phpapp02/85/Go-pathway-interaction-integration-7-320.jpg)
![Automating integration using cross-products – pathway databasesWe can also automatically map:catalysis terms [165*]transport [373]binding [133]phosphorylation and other modificationsmetabolism [278]signaling…All this relies on different cross-product filesAny pathway database that exports BioPax-OWL can be usedE.ghumancyc, mousecyc, pathwaycommons, …*Numbers for Reactome-human](https://image.slidesharecdn.com/go-pathway-interaction-integration-100512225402-phpapp02/85/Go-pathway-interaction-integration-8-320.jpg)
![Automating integration using cross-products – interaction databasesFIGFVEGFRbindshas_functionis_a[Term]id: GO:0043184name: vascular endothelial growth factor receptor 2 bindingintersection_of: GO:0005488 ! bindingintersection_of:results_in_binding_ofPRO:000002112! VEGFR 2](https://image.slidesharecdn.com/go-pathway-interaction-integration-100512225402-phpapp02/85/Go-pathway-interaction-integration-9-320.jpg)
Go pathway-interaction-integration
- 1. Integration of GO, Pathway data and Interaction dataChris MungallPeter D’Eustachio
- 2. The GO was originally intended to integrate databasesHow are we doing?Interoperability of genomic databases is limited by this lack of progress, and it is this major obstacle that the Gene Ontology (GO) Consortium was formed to addressGene Ontology: Tool for theUnificationofBiology. Nat Genet 2000SGDFBGOA
- 3. GOThe GO was originally intended to integrate databasesHow are we doing?Not as well as we could!GOSGDFBGOAPathway CommonsIMEXReactomeCyc…BioGRIDIntact…
- 4. Integration enhances analyses and reduces workloadDivision of laborleave specialized curation to specialized systems biology databasesbut data needs to be re-combined to prevent siloingGO is an invaluable single-stop shop for term enrichment etcCan we quantify how integrating with systems biology databases helps users?Yes! We can do the experiment:GO term enrichment analysis on all MolSigDBwithReactome annotationsAlso include Reactome inputs/outputs, not currently in GOAwithoutReactomeannotations
- 5. Integration enhances analyses GOA+R: Many p-values will significantly improvedRecapitulated biologically valid results that would have been suppressed had one single resource been usedExamples:Genes down-regulated in Alzheimers
- 6. How are we currently integrating systems biology datasets?Interaction dataCurrently Intact, soon IMEX“protein binding” and “self-protein binding” only (+with)Pathway dataCurrently ReactomeonlyLoses much of what is in ReactomeE,g,inputs and outputs Manually curated GO<->Reactome linksincompletenot always to the most specific termlabor-intensivebecome stale over timeother pathway databases?This can be improved!
- 7. Automating integration using cross-product definitions – pathway databases[Term]id: GO:0015871name: choline transportintersection_of: GO:0006810 ! transportintersection_of:results_in_transport_ofCHEBI:15354 ! choline
- 8. Automating integration using cross-products – pathway databasesWe can also automatically map:catalysis terms [165*]transport [373]binding [133]phosphorylation and other modificationsmetabolism [278]signaling…All this relies on different cross-product filesAny pathway database that exports BioPax-OWL can be usedE.ghumancyc, mousecyc, pathwaycommons, …*Numbers for Reactome-human
- 9. Automating integration using cross-products – interaction databasesFIGFVEGFRbindshas_functionis_a[Term]id: GO:0043184name: vascular endothelial growth factor receptor 2 bindingintersection_of: GO:0005488 ! bindingintersection_of:results_in_binding_ofPRO:000002112! VEGFR 2
- 10. Automated Integration: ResultsReactomeEvaluation in progressMany manually assigned equivalencies recapitulatedInferred equivalencies differed in some casessometimes better than manually assignedsometimes required info not in biopax exportongoing discussionsBioGridnot evaluated (all trivial)inferred annotations improve some enrichment resultsE.g. Brentani angiogenesis gene sets, increased enrichment for VEGFR bindingObvious but useful as proof of concept
- 11. Conclusions and future workWe can be more efficient:Coordinate with systems bio databases to divide laborPrevent siloing through semi-automated integrationGO acts as a high-level ‘window’ on systems biology databasesStill to be done:Make integration tool production-readyReconcile existing mis-alignments, particularly signalinghighly inconsistent between GO and ReactomeExplore open questions – e.g. auto-generate terms?Finish cross-products, they are vitalparticular PRO, CHEBI
Editor's Notes
- #3: The Gene Ontology was created as a response to the need to address the need for interoperability in genomic databases in the wake of the sequencing of the first metazoan genomes. In the paper Gene Ontology: tool for the unification of biology published nearly ten years ago, Ashburner et al state: Progress in the way that biologists describe and conceptualize the shared biological elements has not kept pace with sequencing . . . Interoperability of genomic databases is limited by this lack of progress, and it is this major obstacle that the Gene Ontology (GO) Consortium was formed to address [25].The GO has since become the de-facto terminological standard for functional annotation, and its success is evident in the popularity of GO-based class enrichment analyses. However, the intervening ten years have witnessed an explosion of interest in systems biology, with a concomitant increase in the number of databases providing information on interactions and pathways, including Reactome, Nature Signaling, PANTHER [26], BIND, BioGRID and HumanCyc (the EcoCyc metabolic pathway database preceded GO [27]). These databases each have their own individual data models and schemas, creating an interoperability problem. This has partly been mitigated by the adoption of BioPAX as a standard exchange format, which allows the aggregation of multiple pathway databases in single “one-stop shopping” warehouses, such as the Pathway Knowledge Base [28], Pathway Commons, and WikiPathways. However, the data is still only partially integrated, and if a researcher wishes to obtain a comprehensive view of a pathway they must still examine multiple records, in addition to GO annotations
- #4: loss in pathway db transition
- #5: their data models capture more.
- #10: also via col16
- #11: PRO does not yet have Ras etc