NIFSTD - A Comprehensive Ontology for Neuroscience

1. NIFSTD - A Comprehensive Ontology for Neuroscience Fahim T. Imam, Sarah M. Maynard, Stephen D. Larson, Maryann E. Martone, Amarnath Gupta, Jeffery S. Grethe Neuroscience Information Framework, University of California, San Diego INTRODUCTION Class Definitions. OBO Foundry practice requires all concepts receive clear and specific human readable definitions structured in Aristotelian form: “A is a B which has C”, e.g., “the globus pallidus is a brain region which is found within the basilar region of the vertebrate telencephalon.” Without definitions, there is no way to guide the annotation choices made by curators which leads to terms being used in unanticipated ways that confound concept-based data federation. As is quite common even with well-utilized terminologies, not all terms in NIFSTD have definitions at this time. The curation_status annotation property tracks entities that are still lacking final definitions; this property is updated as definitions are added (uncurated) and finalized (curated). Lexical Variants.NIFSTD includes a variety of accepted synonymous terms to identify a distinct concept. These terms serve as an aid to annotators and help when using the ontology to index a large text corpus that often employ a variety of synonyms to identify a specific concept. Lexical variants also include alternative spellings and antiquated terms no longer in common use. Mapping Existing Equivalent Concepts. In addition to synonymous terms, external identifiers are included from one or more external sources where equivalent concepts exist, e.g., UMLS CUIs, NCBI Taxonomy IDs, or NeuroNames IDs. This inter-terminology mapping helps to enable automatic data federation and querying against existing data sets already annotated with such IDs. Representation of Concept Relations. NIFSTD utilizes the OBO-RO for specifying relationships between entities that are unambiguous, distinct, and constrained. Concepts across domains are related to one another through a set of specific object properties specified in the OBO-RO such as located in, contains, inheres in, participates in, etc. These relational properties mostly exist as inverse pairs—e.g., part of and has part (see below for more detail on relations). Use of the OBO-RO serves both to separate the representation of different types of relations (e.g., “is a” vs. “part of”) and to limit to proliferation of relation types. The former requirement is critical to enabling maximal algorithmic parseability of relations. For instance, it has been documented that the computational power of the Gene Ontology is limited by the fact that it mixes the depiction of “is a” and “part of” relations in a single hierarchical graph (Smith et al. 2003). At the same time, it is equally vital that the number of relations not be overly expansive, as each relation brings with it a computational burden – the computer code required to interpret the meaning of that relation. As a core component of Neuroscience Information Framework (NIF) project (http://neuinfo.org), NIF Standard (NIFSTD) was envisioned as a set of modular ontologies that provide a comprehensive collection of terminologies to describe neuroscience relevant data and resources. We present here on the structure, design principles and current state of NIFSTD. The NIFSTD is a critical constituent in the NIF project to enable an effective concept-based search mechanism against a diverse collection of neuroscience resources. The overall ontology has been assembled in a form that promotes reuse of standard ontologies in biomedical domain, easy extension and modification over the course of its evolution. STRUCTURE AND DESIGN PRINCIPLES The NIFSTD is constructed according to best practices closely followed by the Open Biological Ontology (OBO) community. It was built in a modular fashion, each covering a distinct orthogonal neuroscience relevant domain. A list of this module is listed in Table 1. NIFSTD avoids duplication of efforts by conforming to standards that promote reuse. The modules are standardized to the same upper level ontologies, the Basic Formal Ontology (BFO), OBO Relations Ontology (OBO-RO), and the Ontology of Phenotypic Qualities (PATO). Through the use of these foundational and generic ontologies, each of these modules was represented in a standardized manner. This approach not only follows the powerful modularization ontology design pattern (http://odps.sourceforge.net/), but can also be more easily extended to provide highly nuanced representations to meet the need of emerging neuroscientific research domains. • Bridge Files and Object Properties. In order to maintain the orthogonal nature of the ontology domain modules, the cross-domain relations are specified in separate ontology bridge files rather than incorporated into the individual modules. In this way, the main domain files—e.g., anatomy, cell type, disease, etc.—remain independent of one another. Using these bridge files, the dependencies need only be introduced by those applications that require them, such as the NIF system, which requires a description of the anatomical location of nerve cell types. These relations currently reside in the NIF Cell module, but they are being moved to a separate files, called “bridge files” (see “Results” section for explanation), so that other applications which seek to use the underlying nerve cell domain ontology, but do not necessarily intend to import those relations, can do so. Bridge files can also choose either to import the referenced domain ontologies in their entirety or to take a more minimal approach and simply declare the classes they need to reference. • Importing a New Ontology. The process of importing a new vocabulary into the NIFSTD varies depending upon its state (Table 1) as follows: • If a vocabulary already uses OWL, the OBO-RO and the BFO and is orthogonal to existing modules, the import simply involves adding an owl:import statement to the main ontology file (nif.owl). • If an existing orthogonal ontology is in OWL but does not use the same foundational ontologies as NIFSTD, then an ontology bridge file is constructed declaring the deep level semantic equivalencies such as foundational objects and processes. Relations are drawn from the OBO-RO as needed. • If the external terminology is organized but has not been represented in OWL, or does not use the same foundation as NIFSTD, then the terminology is adapted to OWL/RDF in the context of the NIFSTD foundational layer ontologies. Viewing the NIFSTD Vocabularies. The NIFSTD vocabularies are available as owl files which may be viewed using Protégé or similar ontology tools. However, these tools generally require a fair amount of expertise to use. To create more human friendly viewing environments, NIFSTD is also available through NCBO BioPortal. It supports searching for specific terms, browse the overall ontology concept tree, select specific concepts to display in the graph viewer, and view associated concept properties. Within the NIF, NIFSTD is served through an ontology management system called OntoQuest. OntoQuest generates an OWL-compliant relational schema and supports operations for navigating, path finding, hierarchy exploration, and term searching in ontological graphs. NIFSTD and NeuroLex Wiki. We strive to balance between the involvement of the neuroscience community for domain expertise and knowledge engineering community for ontology expertise when constructing the NIFSTD. The wiki version of NIFSTD, the NeuroLex (http://neurolex.org) has been developed as the easy entry point for the broader community to access, annotate, edit and enhance the core NIFSTD lexicon. The peer reviewed contributions in the media wiki are later implanted in NIFSTD OWL modules in a regular basis. We envision NeuroLex wiki to be the main entry point to NIFSTD contents for the general users and domain experts to view, annotate and contribute to the overall lexicon. Table 1: Domains covered by NIFSTD, along with the vocabularies imported from external sources and the corresponding NIFSTD OWL module. • Representation Language. The NIFSTD ontology (http://purl.org/nif/ontology/nif.owl) is expressed in Web Ontology Language (OWL). The current use of OWL for representing the NIFSTD semantic framework provides both the ability to employ current OWL and RDF tools to assemble and edit the ontology, as well as a means to support a rich semantic mining capability to NIF in the future. NIFSTD holds to the OWL Description Logic (OWL-DL) dialect to ensure computational decidability and support of automated reasoning through the use of a common DL reasoners such as Pallet and Fact++. • Re-use of Available Distilled Knowledge Sources. Wherever possible, existing terminologies and ontologies were reused to cover domains that were required by the Neuroscience community (Table 1). These community vocabularies were culled from a variety of sources, ranging from fully structured ontologies to loosely structured controlled vocabularies. Table 2 highlights these source ontologies which were either imported directly or adopted into different NIFSTD modules. Also indicated in Table 1 is whether the source was in OWL or needed to be adapted, the number of unique classes (concepts) under each domain/subdomain and any comments about the import • Distinct, Orthogonal Concept Domains. Each of the OWL modules in NIFSTD consists of a conceptually orthogonal or distinct domain (Table 1). Orthogonality is one of the primary OBO Foundry principles critical to ensuring maximal re-usability of the ontology. The modularity helps minimize dependencies and ensure re-use by enabling users to accept only those domains they need for annotating. If an ontology contains one or more domains overlapping with an existing module, files must be mapped extensively to specify semantic equivalencies thus creating an added dependency and curatorial burden. • Single Inheritance. Each class within the NIFSTD modules follows single inheritance principle. This promotes the classes to be univocal and avoids ambiguities. However, classes with multiple parents can be derived via automated classification on defined classes i.e., asserted classes with logical necessary and sufficient conditions. • Unique Concept Identifiers and Supported Annotations. Each entity in NIFSTD is identified by a unique identifier and is accompanied by a variety of supporting annotations such as a preferred label, definition, synonymous terms, links to equivalent terms in other terminologies, and other lexical variants (Table 3). These properties were developed largely through the import of similar properties from the Dublin Core Metadata and the Simple Knowledge Organization System (SKOS). Our policy on NIFSTD class identifiers is as follows. • If a module was imported from an OBO Foundry ontology that uses BFO as its foundational ontology, the class names (i.e., identifiers) remain unchanged. As many modules were imported directly from BIRNLex and BIRNLex follows the OBO foundry principles the prefix birnlex_XXXX is frequently used. • Any extensions added by NIF to an imported ontology are identified by the nifextprefix (NIF extension). If an imported ontology was not OBO compliant, e.g., used a string as a class name, was not in OWL or had to be refactored according to BFO, NIF assigns its own class name, and the mapping to the source concept is maintained through the annotation properties, e.g, NeuroNamesID: 342. • The identifiers for the new classes in NIFSTD are prefixed by nlx(NeuroLex) followed by an extension that indicates the core module, e.g., nlx_cell_xxxx and nlx_mol_xxxx represent two class identifiers for the Cell and Molecule modules respectively. • Following the semantic web practice, NIFSTD uses complete Universal Resource Identifiers (URIs) to maintain the identity of a given entity. In the case of a class in NIFSTD, the complete URI is the URI for the OWL module where it resides along with the specific ID (or local name in XML) for the class within that file—e.g., http:// purl.org/nif/NIF-Anatomy.owl#birnlex_1699 is the URI for middle cerebellar peduncle. NIFSTD Development Workflow. The current NIFSTD development/curation workflow includes the tasks mentioned in each of the rectangular boxes followed by a number as in figure 3: Add/Edit NeuroLex Terms/Categories: This step involves various NIF users/ group who are interested to add, update, enhance, or annotate the current NIF vocabularies through NeuroLex. NeuroLex wiki serves as the main entry point/ collaborative interface for implementing changes in the NIFSTD ontology. Bulk Upload of Terms: Depending on the number and nature of terms (i.e., adding new large sub-tree of an existing NIFSTD class, or new classes with known parents for a specific NIF module etc.), we can have bulk upload of terms that requires creating too many categories/pages in NeuroLex Wiki by hand otherwise. These requests can be made through a spreadsheet containing the terms with known parents and annotations. Identify Valid Contribution: This step involves identifying the contributions in the previous steps that are valid according to the NIF domain experts. Every contribution in the NeuroLex requires this step before they get implemented in the actual NIFSTD ontology. Valid contributions are identified based on certain criteria such as relevance to neuroscience research, source, consistency, appropriateness of the hierarchy etc. For the newly added categories this step would make sure that the terms are actually new and not the synonyms or duplicates of the existing NIF concepts. Conclusion Currently covering about 20,000+ concepts includes both classes and synonyms, the NIFSTD continues to evolve to incorporate new modules and contents as well as implementing more detailed and useful cross-domain relations that follow ontology development best practices. 4. Update NIFSTD (testing): This step involves updating the actual NIFSTD OWL files or creating new OWL files in testing environment based on the update of contents from previous steps. 5. Testing in OntoQuest: After each significant updates in the owl files, the NIFTD OWL implementation goes for OntoQuest testing in staging server for feedback. 6. Testing in BioPortal: After each significant updates in the owl files the NIFSTD OWL implementation is tested in BioPortal staging environment for feedback. 7. Keep persistent links to older versions:After positive feedbacks from Step 5 and 6, we archive the links to the old owl files and post the links to the Project wiki. Figure : NIFSTD Development/ Curation Workflow Tasks 8-13 involves updating the NIFSTD production version, updating the NIFSTD project wiki page with release notes with version specific major changes and additions of the new contents in NIFTSD, Updating OntoQuest and BioPortal production versions, and updating the textpresso repository of vocabularies with the newly added terms in NIFSTD. Neuroscience Information Framework http://neuinfo.org