The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration.

The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or 'ontologies'. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium is pursuing a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing coordinated reform, and new ontologies are being created on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable and logically well formed and to incorporate accurate representations of biological reality. We describe this OBO Foundry initiative and provide guidelines for those who might wish to become involved.

NeuroTerrain--a client-server system for browsing 3D biomedical image data sets.

BACKGROUND: Three dimensional biomedical image sets are becoming ubiquitous, along with the canonical atlases providing the necessary spatial context for analysis. To make full use of these 3D image sets, one must be able to present views for 2D display, either surface renderings or 2D cross-sections through the data. Typical display software is limited to presentations along one of the three orthogonal anatomical axes (coronal, horizontal, or sagittal). However, data sets precisely oriented along the major axes are rare. To make fullest use of these datasets, one must reasonably match the atlas' orientation; this involves resampling the atlas in planes matched to the data set. Traditionally, this requires the atlas and browser reside on the user's desktop; unfortunately, in addition to being monolithic programs, these tools often require substantial local resources. In this article, we describe a network-capable, client-server framework to slice and visualize 3D atlases at off-axis angles, along with an open client architecture and development kit to support integration into complex data analysis environments. RESULTS: Here we describe the basic architecture of a client-server 3D visualization system, consisting of a thin Java client built on a development kit, and a computationally robust, high-performance server written in ANSI C++. The Java client components (NetOStat) support arbitrary-angle viewing and run on readily available desktop computers running Mac OS X, Windows XP, or Linux as a downloadable Java Application. Using the NeuroTerrain Software Development Kit (NT-SDK), sophisticated atlas browsing can be added to any Java-compatible application requiring as little as 50 lines of Java glue code, thus making it eminently re-useable and much more accessible to programmers building more complex, biomedical data analysis tools. The NT-SDK separates the interactive GUI components from the server control and monitoring, so as to support development of non-interactive applications. The server implementation takes full advantage of data center's high-performance hardware, where it can be co-localized with centrally-located, 3D dataset repositories, extending access to the researcher community throughout the Internet. CONCLUSION: The combination of an optimized server and modular, platform-independent client provides an ideal environment for viewing complex 3D biomedical datasets, taking full advantage of high-performance servers to prepare images and subsets of associated meta-data for viewing, as well as the graphical capabilities in Java to actually display the data.

Advancing translational research with the Semantic Web.

BACKGROUND: A fundamental goal of the U.S. National Institute of Health (NIH) "Roadmap" is to strengthen Translational Research, defined as the movement of discoveries in basic research to application at the clinical level. A significant barrier to translational research is the lack of uniformly structured data across related biomedical domains. The Semantic Web is an extension of the current Web that enables navigation and meaningful use of digital resources by automatic processes. It is based on common formats that support aggregation and integration of data drawn from diverse sources. A variety of technologies have been built on this foundation that, together, support identifying, representing, and reasoning across a wide range of biomedical data. The Semantic Web Health Care and Life Sciences Interest Group (HCLSIG), set up within the framework of the World Wide Web Consortium, was launched to explore the application of these technologies in a variety of areas. Subgroups focus on making biomedical data available in RDF, working with biomedical ontologies, prototyping clinical decision support systems, working on drug safety and efficacy communication, and supporting disease researchers navigating and annotating the large amount of potentially relevant literature. RESULTS: We present a scenario that shows the value of the information environment the Semantic Web can support for aiding neuroscience researchers. We then report on several projects by members of the HCLSIG, in the process illustrating the range of Semantic Web technologies that have applications in areas of biomedicine. CONCLUSION: Semantic Web technologies present both promise and challenges. Current tools and standards are already adequate to implement components of the bench-to-bedside vision. On the other hand, these technologies are young. Gaps in standards and implementations still exist and adoption is limited by typical problems with early technology, such as the need for a critical mass of practitioners and installed base, and growing pains as the technology is scaled up. Still, the potential of interoperable knowledge sources for biomedicine, at the scale of the World Wide Web, merits continued work.

Brain spatial normalization.

Neuroanatomical informatics, a subspecialty of neuroinformatics, focuses on technological solutions to neuroimage database access. Its current main goal is an image-based query system that is able to retrieve imagery based on anatomical location. Here, we describe a set of tools that collectively form such a solution for sectional material and that are available to investigators to use on their own data sets. The system accepts slide images as input and yields a matrix of transformation parameters that map each point on the input image to a standardized 3D brain atlas. In essence, this spatial normalization makes the atlas a spatial indexer from which queries can be issued simply by specifying a location on the reference atlas. Our objective here is to familiarize potential users of the system with the steps required of them as well as steps that take place behind the scene. We detail the capabilities and the limitations of the current implementation and briefly describe the enhancements planned for the near future.

A guide to building image-centric databases.

There is a paucity of image-centric neuroinformatics infrastructure within the individual investigator's laboratory despite the obvious need for automation and integration of experimental results. Yet, solutions can often be readily built using off-the-shelf databases and associated tools. Doing so simplifies day-to-day research operation and increases throughput. Proper construction of in-house solutions may also expedite community-wide integration of private and public data repositories. Here we describe neuroinformatics approaches at different levels of functionality, required expertise, and size of image datasets. The simplest approach offers ease of image browsing and rudimentary searching. More sophisticated systems provide powerful search capabilities, a means of tracking analysis, and even automated serial processing pipelines. In this practicum, we provide guidance in selecting among the different options.

The NIFSTD and BIRNLex vocabularies: building comprehensive ontologies for neuroscience.

A critical component of the Neuroscience Information Framework (NIF) project is a consistent, flexible terminology for describing and retrieving neuroscience-relevant resources. Although the original NIF specification called for a loosely structured controlled vocabulary for describing neuroscience resources, as the NIF system evolved, the requirement for a formally structured ontology for neuroscience with sufficient granularity to describe and access a diverse collection of information became obvious. This requirement led to the NIF standardized (NIFSTD) ontology, a comprehensive collection of common neuroscience domain terminologies woven into an ontologically consistent, unified representation of the biomedical domains typically used to describe neuroscience data (e.g., anatomy, cell types, techniques), as well as digital resources (tools, databases) being created throughout the neuroscience community. NIFSTD builds upon a structure established by the BIRNLex, a lexicon of concepts covering clinical neuroimaging research developed by the Biomedical Informatics Research Network (BIRN) project. Each distinct domain module is represented using the Web Ontology Language (OWL). As much as has been practical, NIFSTD reuses existing community ontologies that cover the required biomedical domains, building the more specific concepts required to annotate NIF resources. By following this principle, an extensive vocabulary was assembled in a relatively short period of time for NIF information annotation, organization, and retrieval, in a form that promotes easy extension and modification. We report here on the structure of the NIFSTD, and its predecessor BIRNLex, the principles followed in its construction and provide examples of its use within NIF.

Data federation in the Biomedical Informatics Research Network: tools for semantic annotation and query of distributed multiscale brain data.

The broadly defined mission of the Biomedical Informatics Research Network (BIRN, www.nbirn.net) is to better understand the causes human disease and the specific ways in which animal models inform that understanding. To construct the community-wide infrastructure for gathering, organizing and managing this knowledge, BIRN is developing a federated architecture for linking multiple databases across sites contributing data and knowledge. Navigating across these distributed data sources requires a shared semantic scheme and supporting software framework to actively link the disparate repositories. At the core of this knowledge organization is BIRNLex, a formally-represented ontology facilitating data exchange. Source curators enable database interoperability by mapping their schema and data to BIRNLex semantic classes thereby providing a means to cast BIRNLex-based queries against specific data sources in the federation. We will illustrate use of the source registration, term mapping, and query tools.

Federated access to heterogeneous information resources in the Neuroscience Information Framework (NIF).

The overarching goal of the NIF (Neuroscience Information Framework) project is to be a one-stop-shop for Neuroscience. This paper provides a technical overview of how the system is designed. The technical goal of the first version of the NIF system was to develop an information system that a neuroscientist can use to locate relevant information from a wide variety of information sources by simple keyword queries. Although the user would provide only keywords to retrieve information, the NIF system is designed to treat them as concepts whose meanings are interpreted by the system. Thus, a search for term should find a record containing synonyms of the term. The system is targeted to find information from web pages, publications, databases, web sites built upon databases, XML documents and any other modality in which such information may be published. We have designed a system to achieve this functionality. A central element in the system is an ontology called NIFSTD (for NIF Standard) constructed by amalgamating a number of known and newly developed ontologies. NIFSTD is used by our ontology management module, called OntoQuest to perform ontology-based search over data sources. The NIF architecture currently provides three different mechanisms for searching heterogeneous data sources including relational databases, web sites, XML documents and full text of publications. Version 1.0 of the NIF system is currently in beta test and may be accessed through http://nif.nih.gov.

iTools: a framework for classification, categorization and integration of computational biology resources.

The advancement of the computational biology field hinges on progress in three fundamental directions--the development of new computational algorithms, the availability of informatics resource management infrastructures and the capability of tools to interoperate and synergize. There is an explosion in algorithms and tools for computational biology, which makes it difficult for biologists to find, compare and integrate such resources. We describe a new infrastructure, iTools, for managing the query, traversal and comparison of diverse computational biology resources. Specifically, iTools stores information about three types of resources--data, software tools and web-services. The iTools design, implementation and resource meta-data content reflect the broad research, computational, applied and scientific expertise available at the seven National Centers for Biomedical Computing. iTools provides a system for classification, categorization and integration of different computational biology resources across space-and-time scales, biomedical problems, computational infrastructures and mathematical foundations. A large number of resources are already iTools-accessible to the community and this infrastructure is rapidly growing. iTools includes human and machine interfaces to its resource meta-data repository. Investigators or computer programs may utilize these interfaces to search, compare, expand, revise and mine meta-data descriptions of existent computational biology resources. We propose two ways to browse and display the iTools dynamic collection of resources. The first one is based on an ontology of computational biology resources, and the second one is derived from hyperbolic projections of manifolds or complex structures onto planar discs. iTools is an open source project both in terms of the source code development as well as its meta-data content. iTools employs a decentralized, portable, scalable and lightweight framework for long-term resource management. We demonstrate several applications of iTools as a framework for integrated bioinformatics. iTools and the complete details about its specifications, usage and interfaces are available at the iTools web page http://iTools.ccb.ucla.edu.

The neuroscience information framework: a data and knowledge environment for neuroscience.

With support from the Institutes and Centers forming the NIH Blueprint for Neuroscience Research, we have designed and implemented a new initiative for integrating access to and use of Web-based neuroscience resources: the Neuroscience Information Framework. The Framework arises from the expressed need of the neuroscience community for neuroinformatic tools and resources to aid scientific inquiry, builds upon prior development of neuroinformatics by the Human Brain Project and others, and directly derives from the Society for Neuroscience's Neuroscience Database Gateway. Partnered with the Society, its Neuroinformatics Committee, and volunteer consultant-collaborators, our multi-site consortium has developed: (1) a comprehensive, dynamic, inventory of Web-accessible neuroscience resources, (2) an extended and integrated terminology describing resources and contents, and (3) a framework accepting and aiding concept-based queries. Evolving instantiations of the Framework may be viewed at http://nif.nih.gov , http://neurogateway.org , and other sites as they come on line.

A formal ontology of subcellular neuroanatomy.

The complexity of the nervous system requires high-resolution microscopy to resolve the detailed 3D structure of nerve cells and supracellular domains. The analysis of such imaging data to extract cellular surfaces and cell components often requires the combination of expert human knowledge with carefully engineered software tools. In an effort to make better tools to assist humans in this endeavor, create a more accessible and permanent record of their data, and to aid the process of constructing complex and detailed computational models, we have created a core of formalized knowledge about the structure of the nervous system and have integrated that core into several software applications. In this paper, we describe the structure and content of a formal ontology whose scope is the subcellular anatomy of the nervous system (SAO), covering nerve cells, their parts, and interactions between these parts. Many applications of this ontology to image annotation, content-based retrieval of structural data, and integration of shared data across scales and researchers are also described.