Izant openscience
- 1. Why we do itJonathan IzantVP, Sage BionetworksOpen Science Summit 31 July 2010www.sagebase.org
- 2. denial
- 3. Genomics does not yet teach us muchPharma drug development is brokenStandards of care are inadequateAcademics limit open access
- 5. Gene Regulation circa 1990
- 6. Gene Regulation circa 1996
- 7. Gene Regulation circa 2002
- 8. How is genomic data used to understand biology?RNA amplificationMicroarray hybirdizationDNAVariationTumorsProfiling Approaches“Standard” GWAS ApproachesIdentifies Causative DNA Variation but provides NO mechanismGenome scale profiling provide correlates of disease Many examples BUT what is cause and effect?Tumors Provide unbiased view of molecular physiology as it relates to disease phenotypes
- 10. Provide causal relationships and allows predictionsComplex TraitVariationGene IndextraitDNAVariation8Molecular TraitVariation“Integrated” Genetics Approaches
- 11. The “Rosetta Integrative Genomics Experiment”: Generation, assembly, and integration of data to build models that predict clinical outcomeMerck Inc. Co.5 Year ProgramBased at RosettaTotal Resources >$150MGenerate data needed to build bionetworks
- 12. Assemble other available data useful for building networks
- 13. Integrate and build models
- 14. Test predictions
- 16. Design Predictive MarkersConstructing Bayesian Networks
- 17. Extensive Publications now Substantiating Scientific ApproachProbabilistic Causal Bionetwork Models>60 Publications from Rosetta Genetics Group (~30 scientists) over 5 years including high profile papers in PLoS Nature and Nature Genetics Metabolic Disease"Genetics of gene expression surveyed in maize, mouse and man." Nature. (2003)"Variations in DNA elucidate molecular networks that cause disease." Nature. (2008)"Genetics of gene expression and its effect on disease." Nature. (2008) "Validation of candidate causal genes for obesity that affect..." Nat Genet. (2009)….. Plus 10 additional papers in Genome Research, PLoS Genetics, PLoS Comp.Biology, etcCVD"Identification of pathways for atherosclerosis." Circ Res. (2007) "Mapping the genetic architecture of gene expression in human liver." PLoS Biol. (2008) …… Plus 5 additional papers in Genome Res., Genomics, Mamm.Genome"Integrating genotypic and expression data …for bone traits…" Nat Genet. (2005) “..approach to identify candidate genes regulating BMD…" J Bone Miner Res. (2009) dBoneMethods"An integrative genomics approach to infer causal associations ...” Nat Genet. (2005)"Increasing the power to detect causal associations… “PLoS Comput Biol. (2007)"Integrating large-scale functional genomic data ..." Nat Genet. (2008) …… Plus 3 additional papers in PLoS Genet., BMC Genet.
- 18. OpportunityThe stunning technologies coming will generate heaps of genomic dataBionetworks using integrative genomic approaches can highlight the non-redundant components- can find drivers of the disease and of therapiesNeed to develop ways to host massive amounts of data, evolving representations of disease as represented by these probabilistic causal disease models
- 19. DriversRecognition that the benefits of bionetwork based molecular models of diseases are powerful but that they require significant resourcesAppreciation that it will require decades of evolving representations as real complexity emerges and needs to be integrated with therapeutic interventionsRealizing the donation by Merck might seed a “commons” allowing a potential long term gain to the whole community provided by evolving models of disease built via a contributor network
- 20. Mission14Sage Bionetworks is a non-profit organization with a vision to create a “Commons” where integrative bionetworks are evolved by contributor scientists with a shared vision to accelerate the elimination of human disease
- 21. Sage Bionetworks:a busy first year$5m LSDF GrantPartnership with Merck14 Staff move into Sage Offices at FHCRC1st Sage Commons Congress in SF$8m NCI grant for new CCSBPartnership with Pfizer2009 2010First Board of Directors Meeting501(c)(3)determinationCatalyst Funding from Listwin, CHDI and QuintilesNIH New Institution ReviewFirst NIH grant payment
- 24. Global Coherent Data SetsA data set containing genome-wide DNA variation and intermediate trait, as well as physiological phenotype data across a population of individuals large enough to power association or linkage studies, typically 50 or more individuals. To be coherent, the data needs to be matched with consistent identifiers. Intermediate traits are typically gene expression, but may also include proteomic, metabolomic, and other molecular data. GCDs are current state of knowledge and subject to change as more information becomes available to Sage
- 26. Sage Commons ChallengesStandards (data, annotation)Tools (combining, analyzing)Citation (recognition)InternationalizationPublic Engagement
- 27. Barriers:Designing a simple-to-use model for uploading and processing dataData interoperabilityData standartization, Data Qualityconsistent data format and metadataTools and standards: allow the reosuce to gown and evolve, capture metadata in a standardized way and quality measures and quality controlThe Commons will need to resolve issues surrounding protection of human subjects data if the information is to be widely shared.IRB and protection of human subjectsplatform independenceVisualization toolsbuilding the critical mass of contributorslegal/licensing frameworkenormous curation effort needed to correct for incompatible study designs, incomplete data gatheringAbility to capture structured content
- 28. Problem: ‘Accessible’ data often isn’t
- 31. Collaborators
- 32. Biomedical research developed as a Cottage Industry
- 34. Need for multi-layer mega datasets and the vanishing ‘price’ for genes provides incentive for pre-competitive space for genomics
- 35. Incentives:Sociology and policy. Getting people to share and building trust.IMHO, the central challenge will be community adoption.This is a social (political) experiment/ entreprise as much as a scientific challenge. How to motivate individuals not community inclined might be key.Researcher "Turf" /lack of experience sharingpolitic: competitive funding versus communal goalWillingness by the community to share data and key ancillary information (e.g. pathology/clinical data for profiled samples)We need a team that will take the time to make sure we create a set of tools that can interoperate , rather than a set of tools that perform discrete independent tasks.Changing culture of individual recognition, publication, rewards, incentivesBusiness case for contributing and sharing resources and information is unclear to many, while business case for hoarding them is well articulated and obvious.Buy-in from tool developers, data producers and data usersThe theory is great, the practice needs commitment from a wide variety of players
- 36. The Federation ExperimentStephen FriendSage BionetworksAndrea CalifanoColumbia U.Eric SchadtPacBio - UCSFAtul ButteStanford MedTrey IdekerUCSD
- 37. Sage BionetworksFocused on improving treatment of diseaseWorking through extensive partnerships to enable research and drug developmentCultural challenges may eclipse technical and operational hurtleswww.sagebase.org
Editor's Notes
- #25: This is us (data on 230 attendees from ~100 survey respondents) [[with black background]]
- #26: Funny thing about collaborations… “IT Experts” claim to collaborate with everyone, but only 3 other groups acknowledge collaborating with “IT Experts”[[No return arrows from “Funders”, “Media” and “Policy”]]