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The Neuroscience Information Framework Establishing a practical semantic framework for neuroscience Maryann Martone, Ph. D. University of California, San.

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Presentation on theme: "The Neuroscience Information Framework Establishing a practical semantic framework for neuroscience Maryann Martone, Ph. D. University of California, San."— Presentation transcript:

1 The Neuroscience Information Framework Establishing a practical semantic framework for neuroscience Maryann Martone, Ph. D. University of California, San Diego

2 NIF Team Amarnath Gupta, UCSD, Co Investigator Jeff Grethe, UCSD, Co Investigator 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 Anita Bandrowski, NIF Curator Fahim Imam, NIF Ontology Engineer Karen Skinner, NIH, Program Officer Lee Hornbrook Kara Lu Vadim Astakhov Xufei Qian Chris Condit Stephen Larson Sarah Maynard Bill Bug Karen Skinner, NIH

3 What does this mean? 3D Volumes 2D Images Surface meshes Tree structure Ball and stick models Little squiggly lines Data People Information systems

4 The Neuroscience Information Framework: Discovery and utilization of web-based resources for neuroscience UCSD, Yale, Cal Tech, George Mason, Washington Univ Supported by NIH Blueprint  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”

5 Where do I find… Data Software tools Materials Services Training Jobs Funding opportunities Websites Databases Catalogs Literature Supplementary material Information portals...And how many are there?

6 NIF in action

7 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 Hippocampus OR “Cornu Ammonis” OR “Ammon’s horn”

8 NIF searches across multiple sources of information NIF data federation Independent databases registered with NIF or through web services NIF registry: catalog NIF Web: custom web index (work in progress) NIF literature: neuroscience-centered literature corpus (Textpresso) NIF data federation Independent databases registered with NIF or through web services NIF registry: catalog NIF Web: custom web index (work in progress) NIF literature: neuroscience-centered literature corpus (Textpresso)

9 Guiding principles of NIF Builds heavily on existing technologies (open source tools) Information resources come in many sizes and flavors Framework has to work with resources as they are, not as we wish them to be – Federated system; resources will be independently maintained – Very few use standard terminology or map to ontologies No single strategy will work for the current diversity of neuroscience resources Trying to design the framework so it will be as broadly applicable as possible to those who are trying to develop technologies Interface neuroscience to the broader life science community Take advantage of emerging conventions in search and in building web communities

10 Registering a Resource to NIF Level 1 NIF Registry: high level descriptions from NIF vocabularies supplied by human curators Level 2 Access to deeper content; mechanisms for query and discovery; DISCO protocol Level 3 Direct query of web accessible database Automated registration Mapping of database content to NIF vocabulary by human

11 The NIF Registry Very, very simple model Annotated with NIF vocabularies Resource type Organism Reviewed by NIF curators Very, very simple model Annotated with NIF vocabularies Resource type Organism Reviewed by NIF curators

12 Level 2: Updates and deeper integration DISCO involves a collection of files that reside on each participating resource. These files store information describing: - attributes of the resource, e.g., description, contact person, content of the resource, etc. -> updates NIF registry - how to implement DISCO capabilities for the resource These files are maintained locally by the resource developers and are “harvested” by the central DISCO server. In this way, central NIF capabilities can be updated automatically as resources evolve over time. The developers of each resource choose which DISCO capabilities their resource will utilize Luis Marenco, MD, Rixin Wang, PhD, Perry L. Miller, MD, PhD, Gordon Shepherd, MD, DPhil Yale University School of Medicine

13 DISCO Level 2 Interoperation Level 2 interoperation is designed for resources that have only Web interfaces (no database API). Different resources require different approaches to achieve Level 2 interoperation. Examples are: 1.CRCNS - requires metadata tagging of Web pages 2.DrugBank - requires directed traversal of Web pages to extract data into a NIF data repository 3.GeneNetwork - requires Web-based queries to achieve “relational-like” views using “wrappers”

14 DrugBank Example The DrugBank Web interface showing data about a specific drug (Phentoin).

15 DrugBank Example (continued) This DISCO Interoperation file specifies how to extract data from the DrugBank Web interface automatically.

16 DrugBank Example (continued) A NIF user views data retrieved from DrugBank in response to a query in a transparent, integrated fashion.

17 Level 3 Deep query of federated databases with programmatic interface Register schema with NIF – Expose views of database – Map vocabulary to NIFSTD Currently works with relational and XML databases – RDF capability planned for NIF 2.5 (April 2010) Works with NIF registry: databases also annotated according to data type and biological area

18 Integrated views and gene search

19 Is GRM1 in cerebral cortex? NIF system allows easy search over multiple sources of information Well known difficulties in search Inconsistent and sparse annotation of scientific data Many different names for the same thing No standards for data exchange or annotation at the semantic level – Lack of standards in data annotation require a lot of human investment in reconciling information from different sources Allen Brain Atlas MGD Gensat

20 Cerebral Cortex AtlasChildrenParent GenepaintNeocortex, Olfactory cortex (Olfactory bulb; piriform cortex), hippocampus Telencephalon ABACortical plate, Olfactory areas, Hippocampal Formation Cerebrum MBAT (cortex)Hippocampus, Olfactory, Frontal, Perirhinal cortex, entorhinal cortex Forebrain MBLDoesn’t appear 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 Modular ontologies for neuroscience  NIF covers multiple structural scales and domains of relevance to neuroscience  Incorporated existing ontologies where possible; extending them for neuroscience where necessary  Normalized under the Basic Formal Ontology: an upper ontology used by the OBO Foundry  Based on BIRNLex: Neuroscientists didn’t like too many choices  Cross-domain relationships are being built in separate files  Encoded in OWL-DL, but also maintained in a Wiki form, a relational database form and any other way it is needed NIFSTD NS Function Molecule Investigation Subcellular Anatomy Macromolecule Gene Molecule Descriptors Techniques Reagent Protocols Cell Instruments Bill Bug NS Dysfunction Quality Macroscopic Anatomy Macroscopic Anatomy Organism Resource

22 How are ontologies used? Search: query expansion – Synonyms – Related classes – “concept based queries” Annotation: – Resource categorization – Entity mapping Ranking of results – NIF Registry; NIF Web

23 Concept-based search: Entity mapping Brodmann area 3 Brodmann.3 Synonyms and explicit mapping of database content help smooth over terminology differences and custom terminologies

24 Concept-based query: GABAergic neuron Simple search will not return examples of GABAergic neurons unless the data are explicitly tagged as such Too many possible classifications to get them all by keywords Classes are logically defined in NIF ontology i.e., a GABAergic neuron is any neuron that uses GABA as a neurotransmitter

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26 Reclassification based on logical definitions: GABA neuron is any member of class neuron that has neurotransmitter GABA NIF Bridge File NIF Cell NIF Molecule Define a set of properties that relate neuron classes to molecule classes, e.g., Neuron Has neurotransmitter Purkinje cell is a Neuron Purkinje cell has neurotransmitter GABA

27 NIF Bridge Files: Defining classes to enhance neuroscience search Neuron by neurotransmitter Neuron by brain region Neuron by circuit role Neuron by morphology Molecule by role Neurotransmitter Drug Drug of abuse

28 NIF 2.5: Linking NIF entities

29 NIF Cards: Linked data

30 NIF 3.0+: NIF Annotation Standards Tying keyword search to quantitative definitions NIF is developing mappings between “data types” (e.g., age) and NIF annotation standards, e.g., adult Query string: adult mouse – Translation: Adult mouse = >/= 40 days postnatal

31 Building NIF ontologies: Balancing act Different schools of thought as to how to build vocabularies and ontologies NIF is trying to navigate these waters, keeping in mind: – NIF is for both humans and machines – Our primary concern is data – We have to meet the needs of the community – We have a budget and deadlines Building ontologies is difficult even for limited domains, never mind all of neuroscience, but we’ve learned a few things – Reuse what’s there: trying to re-use URI’s rather than map when possible – Make what you do reusable: adopt best practices where feasible Numerical identifiers, unique labels, single asserted simple hierarchies – Engage the community – Avoid “religious” wars: separate the science from the informatics – Start simple and add more complexity Create modular building blocks from which other things can be built

32 Ontologies, etc Mike Bergman

33 What we’ve learned Strategy: Create modular building blocks that can be knit into many things – Step 1: Build core lexicon (NeuroLex) Classes and their definitions Simple single inheritance and non-controversial hierarchies Each module covers only a single domain Understandable by an average human – Step 2: NIFSTD: standardize modules under same upper ontology OBO compliant in OWL – Step 3: Cre a te intra-domain and more useful hierarchies using properties and restrictions – Brain partonomy – Step 4: Bridge two or more domains using a standard set of relations – Neuron to brain region – Neuron to molecule, e.g., GABAergic neuron

34 Neurolex More human centric More stable class structure – Ontologies take time; many versions are retired-not good for information systems that are using identifiers Synonyms and abbreviations were essential for users Can’t annotate if they can’t find it Can’t use it for search if they can’t find it Facilitates semi-automated mapping Contains subsets of ontologies that are useful to neuroscientists – e.g., only classes in Chebi that neuroscientists use Wanted the community to be able to see it and use it – Simple understandable hierarchies Removed the independent continuants, entity etc Used labels that humans could understand – Even if they were plural (meninges vs meninx)

35 Maintaining multiple versions NIF maintains the NIF vocabularies in different forms for different purposes – Neurolex Wiki: Lexicon for community review and comment – NIFSTD: set of modular OWL files normalized under BFO and available for download – NCBO Bioportal for visibility and mapping services – Ontoquest: NIF’s ontology server Relational store customized for OWL ontologies Materialized inferred hierarchies for more efficient queries

36 NeuroLex Wiki Larson “The human interface” Semantic media wiki

37 NIF Architecture Gupta et al., Neuroinformatics, 2008 Sep;6(3):205-17

38 Summary NIF has tried to adopt a flexible, practical approach to assembling, extending and using community ontologies – We use a combination of search strategies: string based, lexicon-based, ontology-based – We believe in modularity – We believe in starting simple and adding complexity – We believe in single asserted hierarchies and multiple inferred hierarchies – We believe in balancing practicality and rigor NIF is working through the International Neuroinformatics Coordinating Facility (INCF) to engage the community to help build out the Neurolex and start adding additional relations – Neuron registry task force – Structural lexicon

39 Where do we go from here? NIF 2.5 – Automatic expansion of logically defined terms More services – NIF vocabulary services are available now More content More, more, more NIF Blog NIF is learning lessons in practical data integration

40 Musings from the NIF… No single approach, technology, philosophy, tool, platform will solve everything The value of making resources discoverable is not appreciated Developing resources (tools, databases, data) that are interoperable at this point is an act of will Decisions can be made at the outset that will make it easier or harder to integrate We build resources for ourselves and our constituents, not automated agents We get mad when commercial providers don’t make their products interoperable Many times the choice of terminology is based on expediency or who taught you biology rather than deep philosophical differences Ontologies, terminologies, lexicons, thesauri Developing a semantic framework on top of unstable resources is difficult Need to adopt a flexible approach

41 NIF Evolution V1.0: NIF NIF ThenNowLater


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