Data-knowledge transition zones within the biomedical research ecosystem

1. Data-knowledge transition zones within the biomedical research ecosystem Maryann E. Martone, Ph. D. University of California, San Diego

2. • NIF is an initiative of the NIH Blueprint consortium of institutes – What types of resources (data, tools, materials, services) are available to the neuroscience community? – How many are there? – What domains do they cover? What domains do they not cover? – Where are they? • Web sites • Databases • Literature • Supplementary material – Who uses them? – Who creates them? – How can we find them? – How can we make them better in the future? http://neuinfo.org • PDF files • Desk drawers NIF has been surveying, cataloging and tracking the neuroscience resource landscape since < 2008

3. BD2K: Big Data to Knowledge • BD2K - a trans-NIH initiative established to enable biomedical research as a digital research enterprise, to facilitate discovery and support new knowledge, and to maximize community engagement. • BD2K aims to develop the new approaches, standards, methods, tools, software, and competencies that will enhance the use of biomedical Big Data by: – Facilitating broad use of biomedical digital assets by making them discoverable, accessible, and citable – Conducting research and developing the methods, software, and tools needed to analyze biomedical Big Data – Enhancing training in the development and use of methods and tools necessary for biomedical Big Data science – Supporting a data ecosystem that accelerates discovery as part of a digital enterprise http://bd2k.nih.gov/

4. How many resources are there?

5. How do resources get added to the NIF? •NIF curators •Nomination by the community •Semi-automated text mining pipelines NIF Registry Requires no special skills Manual and semi- automated updates •NIF Data Federation •DISCO interop •Requires some programming skill •Open Source Brain < 2 hr •Automated update via NIF DISCO dashboard Low barrier to entry; incremental refinementMarenco et al., 2010; 2014

6. Registry vs Federation: Metadata about resource vs metadata/data in database

7. What resources are available for GRM1? With the thousands of databases and other information sources available, simple descriptive metadata will not suffice

8. THE STATE OF RESEARCH RESOURCES: RESOURCE REGISTRY

9. Database Software Application Data Analysis Service Topical Portal Core Facility Ontology Software Resource Years: Anita Bandrowski and Burak Ozyurt Population, Coverage and Linkage of Resource Registry

10. • Automated text mining is used to look for “web page last updated” or copyright dates – Identified for 570 resources – 373 were not updated within the last 2 years (65%) • Manual review of ~200 resources – 38 not updated within the past 2 years (~20%) – 8 migrated to new addresses or institutions – 7 are no longer in service (~3%) – 3 were deemed no longer appropriate What happens to these resources? The Registry provides a persistent identifier and metadata record for what once existed but no longer does

11. Keeping content up to date Connectome Tractography Epigenetics •New tags come into existence •New resource types come into existence, e.g., Mobile apps •Resources add new types of content •Change name •Change scope •> 7000 updates to the registry last year It’s a challenge to keep the registry up to date; sitemaps, curation, ontologies, community review

12. DATA FEDERATION

13. NIF data federation NIF was designed to accommodate the multiplicity of heterogeneous and distributed data resources, providing deep query of the contents and unified views 250 sources > 800 M records

14. What do you mean by data? Databases come in many shapes and sizes • Primary data: – Data available for reanalysis, e.g., microarray data sets from GEO; brain images from XNAT; microscopic images (CCDB/CIL) • Secondary data – Data features extracted through data processing and sometimes normalization, e.g, brain structure volumes (IBVD), gene expression levels (Allen Brain Atlas); brain connectivity statements (BAMS) • Tertiary data – Claims and assertions about the meaning of data • E.g., gene upregulation/downregulation, brain activation as a function of task • Registries: – Metadata – Pointers to data sets or materials stored elsewhere • Data aggregators – Aggregate data of the same type from multiple sources, e.g., Cell Image Library ,SUMSdb, Brede • Single source – Data acquired within a single context , e.g., Allen Brain Atlas Researchers are producing a variety of information artifacts using a multitude of technologies

15. NIF: A search engine for data

16. NIF Information Framework: Query and alignment • Aggregate of community ontologies with some extensions for neuroscience, e.g., Gene Ontology, Chebi, Protein Ontology • Available as services through NIF and BioPortal NIFSTD Organism NS FunctionMolecule Investigation Subcellular structure Macromolecule Gene Molecule Descriptors Techniques Reagent Protocols Cell Resource Instrument Dysfunction Quality Anatomical Structure NIF uses ontologies to enhance search and discovery but is not constrained by them

17. Find clinical trials that have data available?

18. Current challenge: With so much available, how do I find what I need? • “What genes are upregulated by chronic morphine?” – It depends • Most often use cases require connecting a researcher to relevant data sets and appropriate tools – Depending upon the data and tools, the answers may differ • Many databases have tool bases and workflows that they support – Much value has been added to individual data sets

19. Facets and filters: Progressive refinement of search Facet/Filter Source Category Index Query Addiction Registry Data Gene Gemma Gene Organism Expression level Geo Integrated Expression Literature More effective to start with a general query and use the navigation to refine search

20. Concept Mapper: Alignment and weighting Find:gene cerebellum=find all sources with column mapped to gene that also contain keyword cerebellum; Find:gene Anatomy:cerebellum

21. “Data trails”: Linking data and analysis tools

22. Query across Registry and Federation • Registry and Federation were treated separately, even though Federation comprises views of Registry entries • Experimenting with new combined index

23. SciCrunch: A “social network” for resources • NIF is a general search engine across all of neuroscience • Very powerful for discovery and general browsing • Can perform analytics across the spectrum of biomedical resources • Many communities want to create more focused portals • Specialized for their domain • Restrict the particular sources • Organize the data according to their needs • Use their own branding • How do we create a system that satisfies community needs without creating another silo?

24. Put dkNET here http://dknet.org Autogenerated snippets

25. Where can I find validated antibodies against CART?

26. 1 100 10,000 1,000,000 100,000,00010,000,000,000 SOFTWARE PROTOCOLS PHENOTYPE PATHWAYS MULTIMEDIA MOLECULE MICROARRAY IMAGES GENE DRUGS DATASET CLINICAL TRIALS BRAIN ACTIVATION FOCI ATLAS ANNOTATION All databases in the SciCrunch Federation become immediately available through More Resources

27. Breaking down silos: Community enrichment It’s like a Mendeley for resources!

28. SciCrunch Shared Resources Undiagnosed Disease Program Phenotype RCN One Mind for Research Consortia-Pedia Faster Cures Model Organism Databases Community Outreach Shared curation; shared expertise Resource Identification Portal Aging Neuroscience dkNET Phenotypes NSF Earthcube

29. Making use of community Facet/Filter Source Category Index Community Community Community resources SciCrunch data (all) Gene Gemma Gene Organism Expression level Geo Integrated Expression Literature Brings expertise of community to understanding how to work with data

30. KNOWLEDGE TO DATA: GAP ANALYSIS

31. Looking across the ecosystem: Where are the data? Data Sources Bringing knowledge to data: Gap analysis

32. Forebrain Midbrain Hindbrain 0 1-10 11-100 >101 Data Sources Revealing biases in the dataspace

33. SW Oh et al. Nature 000, 1-8 (2014) doi:10.1038/nature13186 Adult mouse brain connectivity matrix: revenge of the midbrain

34. The tale of the tail “Human neuroimaging typically is performed on a whole brain basis. However, for several reasons tail of the caudate activity can easily be missed. •One reason is limitations in the normalization algorithms, that typically are optimized to maximize accuracy for cortical rather than subcortical structures. ... •A second reason is that standard neuroimaging atlases such as the Harvard- Oxford structural atlas used with neuroimaging analysis programs such as FreeSurfer truncate the caudate at the body, and completely exclude the tail... •A final reason is that the tail of the caudate is close to the hippocampus, and could be misidentified as such especially in tasks involving learning and memory. Therefore, the tail of the caudate may be recruited in additional cognitive tasks, but yet not have been properly identified and reported in the neuroimaging literature” Seger CA. The visual corticostriatal loop through the tail of the caudate: circuitry and function. Front Syst Neurosci. 2013 Dec 6;7:104. doi: 10.3389/fnsys.2013.00104. eCollection 2013.

35. Importance of comprehensive indices: For how many proteins are there antibodies? 0 1-10 11-100 101-1000 1001+ Human, protein coding genes (Entrez Gene) vs # of search results from the antibodyregistry.org Antibodyregistry.orgTrish Whetzel and Anita Bandrowski

36. “The Data Homunculus”

37. Data-Knowledge Mismatch Dutowski et al., 2013: Nature Biotechnology

38. The scourge of neuroanatomical nomenclature •NIF Connectivity: 7 databases containing connectivity primary data or claims from literature on connectivity between brain regions •Brain Architecture Management System (rodent) •Temporal lobe.com (rodent) •Connectome Wiki (human) •Brain Maps (various) •CoCoMac (primate cortex) •UCLA Multimodal database (Human fMRI) •Avian Brain Connectivity Database (Bird) •Total: 1800 unique brain terms (excluding Avian) •Number of exact terms used in > 1 database: 42 •Number of synonym matches: 99 •Number of 1st order partonomy matches: 385

39. 6 parcellation schemes of mouse prefrontal cortex based on Nissl alone Van De Werd HJ1, Uylings HB.. Brain Struct Funct. 2014 Mar;219(2):433-59. doi: 10.1007/s00429-013-0630-

40. How many neuron types are there? NIH funding announcement: BRAIN Initiative: Transformative Approaches for Cell-Type Classification in the Brain “The mammalian brain contains a vast number of cells. These cells are generally grouped within broad classes (e.g., neurons or glia) but it is currently unknown exactly how many classes exist.”

41. Location of Cell Soma Location of dendrites Location of local axon arbor Transition Zones: Neurons and their properties

42. Analysis of Red Links in the Neuron Registry • INCF Project – Neuron Registry • Neurolex.org • Semantic MediaWiki – > 30 experts worldwide – Fill out neuron pages in Neurolex Wiki Soma location Dendrite location Axon location 0 50 100 150 200 250 300 Number Total redlinks easy fixes hard fixes Soma location Dendrite location Axon location Social networks and community sites let us learn things from the collective behavior of contributors  show limits in our knowledge and our knowledge representations

43. Domain Knowledge Ontologies Atlases/Maps Annotation Claims, assertions Registries Derived data Models and simulations Analyses Data Databases Data sets Literature Search and Discovery Cannot try to shoe-horn everything into a single representation or system, but figure out how information (data + knowledge) can flow between them; Knowledge is fluid and will continually update SciCrunch: Creating a Data and Resource Discovery Environment

44. BD2K: Creating a Data Discovery Index • BioCADDIE – Dr. Lucila Ohno- Machado PI – FORCE11: Community engagement piece • What should a data discovery index do? – Task Forces – Pilot projects • How should it be built? http://biocaddie.org BIOMEDICAL AND HEALTH CARE DATA DISCOVERY AND INDEXING ENGINE CENTER

45. NIF team (past and present) Jeff Grethe, UCSD, Co Investigator, Interim PI Amarnath Gupta, UCSD, Co Investigator Anita Bandrowski, NIF Project Leader Gordon Shepherd, Yale University Perry Miller Luis Marenco Rixin Wang David Van Essen, Washington University Erin Reid Paul Sternberg, Cal Tech Arun Rangarajan Hans Michael Muller Yuling Li Giorgio Ascoli, George Mason University Sridevi Polavarum Fahim Imam Larry Lui Andrea Arnaud Stagg Jonathan Cachat Jennifer Lawrence Svetlana Sulima Davis Banks Vadim Astakhov Xufei Qian Chris Condit Mark Ellisman Stephen Larson Willie Wong Tim Clark, Harvard University Paolo Ciccarese Karen Skinner, NIH, Program Officer (retired) Jonathan Pollock, NIH, Program Officer And my colleagues in Monarch, dkNet, 3DVC, Force 11

46. BD2K-K2BD: Data Discovery Index • Accounting of what is available – Comprehensive resource registry – UPC’s for research resources • Information framework – Major concepts contained in data, but also accounting of what happens to data as it flows through the ecosystem (provenance) • Community-based portals into shared data resources – Share expertise – Metrics of trust – Shared curation and upkeep • Two way validation of knowledge to data

47. Registry vs Federation: Metadata about resource vs metadata/data in database With the thousands of databases and other information sources available, simple descriptive metadata will not suffice

48. What have we learned: Grabbing the long tail of small data • NIF is in a unique position to ask questions against the data resource landscape • The data space is not uniform • Data “flows” from one resource to the next – Data is reinterpreted, reanalyzed or added to • Currently very difficult to track data as it moves across the landscape – Makes it difficult to learn from combined efforts

50. Working with and extending ontologies: Neurolex.org http://neurolex.org Larson et al, Frontiers in Neuroinformatics, in press •Semantic MediWiki •Provide a simple interface for defining the concepts required •Light weight semantics-sets of triples •Good teaching tool for learning about semantic integration and the benefits of a consistent semantic framework •Community based: •Anyone can contribute their terms, concepts, things •Anyone can edit •Anyone can link •Accessible: searched by Google •Growing into a significant knowledge base for neuroscience Demo D03

51. Neuron Lexicon: Gauging the state of knowledge in neuroscience • Led by Dr. Gordon Shepherd • > 30 world wide experts • Simple set of properties • Consistent naming scheme • Integrated with Structural Lexicon • Used for annotation in other resources, e.g., NeuroElectro

52. Analyzed Curated GSE13732 Analyzed Mirrored Stable identifiers and annotations allow us to “track” data as it moves Data flows throughout the ecosystem...value is added

53. Analyzed Curated GSE13732 Analyzed Mirrored But…even our standards need standards GSE13732 E-GEOD-13732 GEO:GSE13732 Standard identifier format for all data federation sources; text mining to deal with inconsistencies

54. Same data: different analysis • Gemma: Gene ID + Gene Symbol • DRG: Gene name + Probe ID • Gemma presented results relative to baseline chronic morphine; DRG with respect to saline, so direction of change is opposite in the 2 databases Chronic vs acute morphine in striatum • Analysis: •1370 statements from Gemma regarding gene expression as a function of chronic morphine •617 were consistent with DRG;  over half of the claims of the paper were not confirmed in this analysis •Results for 1 gene were opposite in DRG and Gemma •45 did not have enough information provided in the paper to make a judgment NIF is working to make it easier to find where data has gone and what has been done with it

55. How many do we use? These resources themselves need to be citable

56. Resource Identification Initiative: Linking resources to literature • Have authors supply appropriate identifiers for key resources used within a study such that they are: – Machine processible (i.e., unique identifier that resolves to a single resource) – Outside of the paywall – Uniform across journals and publishers • Pilot project: SciCrunch portal serving identifiers for – Software/databases – Antibodies – Genetically modified organisms Launched February 2014: > 30 journals participating

57. What studies have used...? •>200 articles have appeared to date •>30 journals •Data set being made available to community •> 650 RRID’s •~10% disappeared after copyediting •5% were in error Database available at: https://www.force11.org/node/5635

58. : C Neurolex: > 1 million triples Dr. Yi Zeng: Chinese neural knowledge base NIF Cell Graph This is your brain on computers

Data-knowledge transition zones within the biomedical research ecosystem

Data-knowledge transition zones within the biomedical research ecosystem

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Data-knowledge transition zones within the biomedical research ecosystem