1. The possibility and probability of establishing a global neuroscience information framework: lessons learned from practical experiences in data integration for neuroscience Maryann Martone, Ph. D. University of California, San Diego

2. “Neural Choreography” “A grand challenge in neuroscience is to elucidate brain function in relation to its multiple layers of organization that operate at different spatial and temporal scales. Central to this effort is tackling “neural choreography” -- the integrated functioning of neurons into brain circuits--their spatial organization, local and long-distance connections, their temporal orchestration, and their dynamic features. Neural choreography cannot be understood via a purely reductionist approach. Rather, it entails the convergent use of analytical and synthetic tools to gather, analyze and mine information from each level of analysis, and capture the emergence of new layers of function (or dysfunction) as we move from studying genes and proteins, to cells, circuits, thought, and behavior.... However, the neuroscience community is not yet fully engaged in exploiting the rich array of data currently available, nor is it adequately poised to capitalize on the forthcoming data explosion. “ Akil et al., Science, Feb 11, 2011

3. On the other hand... In that same issue of Science Asked peer reviewers from last year about the availability and use of data About half of those polled store their data only in their laboratories—not an ideal long-term solution. Many bemoaned the lack of common metadata and archives as a main impediment to using and storing data, and most of the respondents have no funding to support archiving And even where accessible, much data in many fields is too poorly organized to enable it to be efficiently used. “...it is a growing challenge to ensure that data produced during the course of reported research are appropriately described, standardized, archived, and available to all.” Lead Science editorial (Science 11 February 2011: Vol. 331 no. 6018 p. 649 )

4. We speak piously of taking measurements and making small studies that will add another brick to the temple of science. Most such bricks just lie around the brickyard. Platt, J.R. (1964) Strong Inference. Science. 146: 347-353. "We now have unprecedented ability to collect data about nature…but there is now a crisis developing in biology, in that completely unstructured information does not enhance understanding” -Sidney Brenner

5. The Encyclopedia of Life A… Access to data has changed over the years Tim Berner-s Lee: Web of data Wikipedia defines Linked Data as "a term used to describe a recommended best practice for exposing, sharing, and connecting pieces of data, information, and knowledge on the Semantic Web using URIs and RDF.” http://linkeddata.org/ datainformationknowledge URIsRDF http://linkeddata.org/ Genban k PDB

6. Are we there yet? We’d like to be able to find: What is known****: What is the average diameter of a Purkinje neuron Is GRM1 expressed In cerebral cortex? What are the projections of hippocampus What genes have been found to be upregulated in chronic drug abuse in adults What studies used my monoclonal mouse antibody against GAD in humans ? Find all instances of spines that contain membrane-bound organelles **** by combining data from different sources and different groups What is not known: Connections among data Gaps in knowledge We’d like it to be really simple to implement and use : – Query interface – Search strategies – Data sources – Infrastructure – Results display – Trust – Context – Analysis tools – Tools for translating existing content into linkable form – Tools for creating new data ready to be linked

7. NIF is an initiative of the NIH Blueprint consortium of institutes 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? What types of resources (data, tools, materials, services) are available to the neuroscience community? How many are there? How many are there? What domains do they cover? What domains do they not cover? What domains do they cover? What domains do they not cover? Where are they? Where are they? Web sites Web sites Databases Databases Literature Literature Supplementary material Supplementary material Who uses them? Who uses them? Who creates them? Who creates them? How can we find them? How can we find them? How can we make them better in the future? How can we make them better in the future? http://neuinfo.org A look into the brickyard PDF files PDF files Desk drawers Desk drawers

8. How many resources are there? NIF Registry: A catalog of neuroscience-relevant resources > 3500 currently described > 1700 databases Another 3000 awaiting curation And we are finding more every day NIF Registry: A catalog of neuroscience-relevant resources > 3500 currently described > 1700 databases Another 3000 awaiting curation And we are finding more every day

9. But we have Google! Current web is designed to share documents Documents are unstructured data Much of the content of digital resources is part of the “hidden web” Wikipedia: The Deep Web (also called Deepnet, the invisible Web, DarkNet, Undernet or the hidden Web) refers to World Wide Web content that is not part of the Surface Web, which is indexed by standard search engines.World Wide WebSurface Webindexedsearch engines

10. A tip of the “resourceome” Microarray 9, 535, 440 Microarray 9, 535, 440 Model organisms 246, 639 Model organisms 246, 639 Connectivity 26, 443 Connectivity 26, 443 Antibodies 890, 571 Antibodies 890, 571 Pathways 43, 013 Pathways 43, 013 Brain Activation Foci 56, 591 Brain Activation Foci 56, 591 65 databases

11. But we have Pub Med! Bulk of neuroscience data is published as part of papers > 20,000,000 Structured vs unstructured information “...it is a growing challenge to ensure that data produced during the course of reported research are appropriately described, standardized, archived, and available to all.” Lead Science editorial (Science 11 February 2011: Vol. 331 no. 6018 p. 649 ) Author, year, journal, keywords Content

12. The Neuroscience Information Framework: Discovery and utilization of web-based resources for neuroscience A portal for finding and using neuroscience resources  A consistent framework for describing resources  Provides simultaneous search of multiple types of information, organized by category  Supported by an expansive ontology for neuroscience  Utilizes advanced technologies to search the “hidden web” http://neuinfo.org UCSD, Yale, Cal Tech, George Mason, Washington Univ Supported by NIH Blueprint Literature Database Federation Registry

13. Neuroscience is unlikely to be served by a few large databases like the genomics and proteomics community Whole brain data (20 um microscopic MRI) Mosiac LM images (1 GB+) Conventional LM images Individual cell morphologies EM volumes & reconstructions Solved molecular structures No single technology serves these all equally well.  Multiple data types; multiple scales; multiple databases A data federation problem

14. NIF Data Federation Too many databases to visit Registry not adequate for finding and using them Capturing content in a few keywords is difficult if not impossible Access to deep content; currently searches over 30 million records from > 65 different databases Flexible tools for resource providers to make their content available as easily and meaningfully as possible Organized according to level of nervous system and data type, e.g., brain activation foci Link to host resource: these databases are independent! Provides simplified and unified views to help users navigate very different resources Common vocabularies Common data models for basic neuroscience data Laying the foundations for data integration for neuroscience

15. What are the connections of the hippocampus?

16. Hippocampus OR “Cornu Ammonis” OR “Ammon’s horn” Query expansion: Synonyms and related concepts Boolean queries Query expansion: Synonyms and related concepts Boolean queries Data sources categorized by “data type” and level of nervous system Simplified views of complex data sources Tutorials for using full resource when getting there from NIF Link back to record in original source

17. What are the connections of the hippocampus? Connects to Synapsed with Synapsed by Input region innervates Axon innervates Projects to Cellular contact Subcellular contact Source site Target site Each resource implements a different, though related model; systems are complex and difficult to learn, in many cases

18. NIF: Minimum requirements to use shared data You (and the machine) have to be able to find it Accessible through the web Structured or semi-structured Annotations You (and the machine) have to be able to use it Data type specified and in a usable form You (and the machine) have to know what the data mean Semantics Context: Experimental metadata Reporting neuroscience data within a consistent framework helps enormously

19. Is GRM1 in cerebral cortex? NIF system allows easy search over multiple sources of information But, we have difficulty finding data Well known difficulties in search Inconsistent and sparse annotation of scientific data Many different names for the same thing The same name means many things “Hidden semantics”: 1 = male; 1 = present; 1=mouse Allen Brain Atlas MGD Gensat

20. Cerebral Cortex AtlasChildrenParent GenepaintNeocortex, Olfactory cortex (Olfactory bulb; piriform cortex), hippocampus Telencephalon Allen Brain AtlasCortical plate, Olfactory areas, Hippocampal Formation Cerebrum MBAT (cortex)Hippocampus, Olfactory, Frontal, Perirhinal cortex, entorhinal cortex Forebrain GENSATNot definedTelencephalon BrainInfofrontal lobe, insula, temporal lobe, limbic lobe, occipital lobe Telencephalon Brainmaps Entorhinal, insular, 6, 8, 4, A SII 17, Prp, SI Telencephalon

21. What is an ontology? Brain Cerebellum Purkinje Cell Layer Purkinje cell neuron has a is a Ontology: an explicit, formal representation of concepts relationships among them within a particular domain that expresses human knowledge in a machine readable form Branch of philosophy: a theory of what is e.g., Gene ontologies

22. What can ontology do for us? Express neuroscience concepts in a way that is machine readable Synonyms, lexical variants Definitions Provide means of disambiguation of strings Nucleus part of cell; nucleus part of brain; nucleus part of atom Rules by which a class is defined, e.g., a GABAergic neuron is neuron that releases GABA as a neurotransmitter Properties Provide universals for navigating across different data sources Semantic “index” Perform reasoning Link data through relationships not just one-to-one mappings Provide the basis for concept-based queries to probe and mine data As a branch of philosophy, make us think about the nature of the things we are trying to describe, e.g., synapse is a site

23. Linking datatypes to semantics: What is the average diameter of a Purkinje neuron dendrite? Branch structure not a tree, not a set of blood vessels, not a road map but a DENDRITE Because anyone who uses Neurolucida uses the same concepts: axon, dendrite, cell body, dendritic spine, information systems can combine the data together in meaningful ways Neurolucida doesn’t, however, tell you that dendrite belongs to a neuron of a particular type or whether this dendrite is a neural dendrite at all ( (Color Yellow) ; [10,1] (Dendrite) ( 5.04 -44.40 -89.00 1.32) ; Root ( 3.39 -44.40 -89.00 1.32) ; R, 1 ( ( 2.81 -45.10 -90.00 0.91) ; R-1, 1 ( 2.81 -45.18 -90.00 0.91) ; R-1, 2 ( 1.90 -46.01 -90.00 0.91) ; R-1, 3 ( 1.82 -46.09 -90.00 0.91) ; R-1, 4 ( 0.91 -46.59 -90.00 0.91) ; R-1, 5 ( 0.41 -46.83 -92.50 0.91) ; R-1, 6 ( ( -0.66 -46.92 -88.50 0.74) ; R-1-1, 1 ( -0.74 -46.92 -88.50 0.74) ; R-1-1, 2 ( -2.15 -47.25 -88.00 0.74) ; R-1-1, 3 ( -2.15 -47.33 -88.00 0.74) ; R-1-1, 4 ( -3.06 -47.00 -87.00 0.74) ; R-1-1, 5 ( -4.05 -46.92 -86.00 0.74) ; R-1-1, 6 Output of Neurolucida neuron trace

24. “A rose by any other name...”: Identity: Entities are uniquely identifiable Name is a meaningless numerical identifier (URI: Uniform resource identifier) Any number of human readable labels can be assigned to it Definition: Genera: is a type of (cell, anatomical structure, cell part) Differentia: “has a” A set of properties that distinguish among members of that class Can include necessary and sufficient conditions Implementation: How is this definition expressed Depending on the nature of the concept or entity and the needs of the information system, we can say more or fewer things Different languages; can express different things about the concept that can be computed upon OWL W3C standard, RDF

25. Comprehensive Ontology NIF covers multiple structural scales and domains of relevance to neuroscience Aggregate of community ontologies with some extensions for neuroscience, e.g., Gene Ontology, Chebi, Protein Ontology Simple, basic “is a : hierarchies that can be used “as is” or to form the building blocks for more complex representations NIFSTD Organism NS Function Molecule Investigation Subcellular structure Macromolecule Gene Molecule Descriptors Techniques Reagent Protocols Cell Resource Instrument Dysfunction Quality Anatomical Structure

26. Query across resources: Snca and striatum NIF uses the NIFSTD ontologies to query across sources that use very different terminologies, symbolic notations and levels of granularity

27. Entity mapping BIRNLex_435 Brodmann.3 Explicit mapping of database content helps disambiguate non-unique and custom terminology

28. Concept-based search: search by meaning Search Google: GABAergic neuron Search Google: GABAergic neuron Search NIF: GABAergic neuron Search NIF: GABAergic neuron NIF automatically searches for types of GABAergic neurons NIF automatically searches for types of GABAergic neurons Types of GABAergic neurons

29. Data mining through interrogation What genes are upregulated by drugs of abuse in the adult mouse? What genes are upregulated by drugs of abuse in the adult mouse? Morphine Increased expression Adult Mouse

30. Integration of knowledge based on relationships Looking for commonalities and distinctions among animal models and human conditions based on phenotypes Sarah Maynard, Chris Mungall, Suzie Lewis NINDS Thalamus Cellular inclusion Midline nuclear group Lewy Body Paracentral nucleus Cellular inclusion

31. And now, the literature The scientific article remains the currency of science Vast majority of neuroscience data is published in the literature Computational biologists like to consume data Neuroscientists like to produce it Two NIF projects: 1) Resource identification from the literature Identifying antibodies used in scientific studies from text 2) Extracting data from tables and supplementary material

32. Neuroscience is fundamentally reliant on antibodies Neuroscientists spend a lot of time searching for antibodies that will work in their system for the target of interest and troubleshooting experiments that didn’t work The scientific literature is a major source of information on antibodies Proposal Use text mining strategies to identify antibodies, protocol type and subject organism from materials and methods section of J. Neuroscience Problem: antibodies

33. Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and from subjects with AD (n11) were obtained from the Rapid Autopsy Program Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and from subjects with AD (n11) were obtained from the Rapid Autopsy Program Immunoblot analysis and antibodies Immunoblot analysis and antibodies The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution, Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000, Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8 mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356, 1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000, Santa Cruz Biotechnology)… The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution, Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000, Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8 mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356, 1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000, Santa Cruz Biotechnology)… Semantic annotation: Entity mapping by human Sato et al., J. Neurosci. 2008 Subject is Human

34. Try this Watson! 95 antibodies were identified in 8 articles 52 did not contain enough information to determine the antibody used Some provided details in another paper And another paper, and another... Failed to give species, clonality, vendor, or catalog number But, many provided the location of the vendor because the instructions to authors said to do so no antibodies had lot numbers associated  We never got to test the algorithms!

35. NIF along with several other large informatics projects recommends that all authors provide vendor and catalog # for all reagents use But...vendors merge and sell each other’s antibodies, making it difficult to track down exactly which reagent was used in some cases Catalog numbers get replaced; many variants on the same product, e.g., HRP-conjugated, 200 ul vs 500 ul Clone names are not unique Universal antibody ID Publishing for the 21 st Century

36. NIF Antibody Registry We have created an antibody registry database Assigns each antibody a persistent identifier to both commercial and non- commercial antibodies ID will persist even if company goes out of business or the antibody is sold by multiple vendors The data model is being formalized into a rigorous ontology in collaboration with others: We negotiated with antibody aggregators to pull data for over 800,000 commercial antibodies, 200 vendors Can be used to register homegrown antibodies as well http://antibodyregistry.org

37. “Find studies that used a rabbit polyclonal antibody against GFAP that recognizes human in immunocytochemisty” Paz et al, J Neurosci, 2010 (AB_310775)

38. Demo 2: Extracting data from tables and supplementary material Challenge: Extract data on gene expression in brain from studies relevant to drug abuse Workflow: Find articles Extract results from tables Standardize results Load into NIF Current DB: 140 tables from 54 articles Andrea Arnaud-Stagg, Anita Bandrowski

39. Gene for tyrosine hydroxylase has increased expression in locus coeruleus of mouse compared to control when given chronic morphine Translations: Upregulated p < 0.05 = increased expression LC = locus coeruleus Probe ID = gene name Extract data and meaning of data from tables

40. Challenges working with tables and supplemental data Difficult data arrangements PDF, JPG, TXT, CSV, XLS Difficult styles: colors, symbols, data arrangements (results combined into one column, multiple comparisons in one table, legends defining values, unclearly described data (eg., unclear significance) Not clear what tables/values represent nothing in paper about the supplementary data file and table has no heading Probe ID’s are given but not gene identifiers No link from supplemental material back to article; lose provenance Results are presented but values of significance unclear Neither curator (nor machine) could distinguish between no difference and not reported

41. What affects SMN1 expression? Researchers often report results in a way where curators cannot extract full information from a study

42. Common theme We are not publishing data in a form that is easy to integrate What we mean isn’t clear to a search engine (or even to a human) We use many different data structures to say the same thing We don’t provide crucial information Searching and navigating across individual resources takes an inordinate amount of human effort Tempus Pecunia Est Painting by Richard Harpum

43. When I talk to neuroscientists (and journal editors)...

44. Collaboration, competition, coordination, cooperation The diversity and dynamism of neuroscience will make data integration challenging always Neural space is vast: No one group or individual can do everything We don’t have to solve everything to make it better Global partnership with room for everyone: Neuroscientists Curators Resource developers Funders Computational biologists Text miners Computer scientists Watson

45. Hopeful signs... Means for sharing data on the web becoming more routine With availability, growing recognition for a role of standards and curation For neuroscience, we now have organizations that can help coordinate NIF, NITRC (http://nitrc.org)http://nitrc.org Neuroimaging Tools and Resource Clearinghouse International Neuroinformatics Coordinating Facility Educate neuroscientists on what is necessary Bring together stakeholders to define what is necessary for interoperation Implement structures and procedures for developing neuroscience resources within a framework http://incf.org

46. We don’t know everything but we do know some things 1. Register your resource with NIF!!!! 3: Be mindful Resource providers: Mindfulness that your resource is contributing data to a global federation Link to shared ontology identifiers where possible Stable and unique identifiers for data Explicit semantics Database, model, atlas Researchers: Mindfulness when publishing data that it is to be consumed by machines and not just your colleagues Accession numbers for genes and species Catalog numbers for reagents Provide supplemental data in a form where it is is easy to re-use 2. Become involved with NIF and INCF

47. Learn about neuroinformatics

48. Many thanks to... Amarnath Gupta, UCSD, Co Investigator Jeff Grethe, UCSD, Co Investigator Anita Bandrowski, NIF Curator Gordon Shepherd, Yale University Perry Miller Luis Marenco David Van Essen, Washington University Erin Reid Paul Sternberg, Cal Tech Arun Rangarajan Hans Michael Muller Giorgio Ascoli, George Mason University Sridevi Polavarum Fahim Imam, NIF Ontology Engineer Karen Skinner, NIH, Program Officer Mark Ellisman Lee Hornbrook Kara Lu Vadim Astakhov Xufei Qian Chris Condit Stephen Larson Sarah Maynard Bill Bug

49. Register your resource to NIF!

50. How old is an adult squirrel? Definitions can be quantitative Definitions can be quantitative Arbitrary but defensible Arbitrary but defensible Qualitative categories for quantitative attributes Qualitative categories for quantitative attributes Best practice to provide ages of subjects, but for query, need to translate into qualitative concepts Best practice to provide ages of subjects, but for query, need to translate into qualitative concepts Jonathan Cachat, Anita Bandrowski

51. But there are no databases for siRNA NIF Registry is probably the most complete accounting we have of what is out there