An evaluation of ontology exchange languages for bioinformatics.

Ontologies are specifications of the concepts in a given field, and of the relationships among those concepts. The development of ontologies for molecular-biology information and the sharing of those ontologies within the bioinformatics community are central problems in bioinformatics. If the bioinformatics community is to share ontologies effectively, ontologies must be exchanged in a form that uses standardized syntax and semantics. This paper reports on an effort among the authors to evaluate alternative ontology-exchange languages, and to recommend one or more languages for use within the larger bioinformatics community. The study selected a set of candidate languages, and defined a set of capabilities that the ideal ontology-exchange language should satisfy. The study scored the languages according to the degree to which they satisfied each capability. In addition, the authors performed several ontology-exchange experiments with the two languages that received the highest scores: OML and Ontolingua. The result of those experiments, and the main conclusion of this study, was that the frame-based semantic model of Ontolingua is preferable to the conceptual graph model of OML, but that the XML-based syntax of OML is preferable to the Lisp-based syntax of Ontolingua.

Seamless integration of biological applications within a database framework.

There are more than two hundred biological data repositories available for public access, and a vast number of applications to process and interpret biological data. A major challenge for bioinformaticians is to extract and process data from multiple data sources using a variety of query interfaces and analytical tools. In this paper, we describe tools that respond to this challenge by providing support for cross-database queries and for integrating analytical tools in a query processing environment. In particular, we describe two alternative methods for integrating biological data processing within traditional database queries: (a) "light-weight" application integration based on Application Specific Data Types (ASDTs) and (b) "heavy-duty" integration of analytical tools based on mediators and wrappers. These methods are supported by the Object-Protocol Model (OPM) suite of tools for managing biological databases.

Advanced query mechanisms for biological databases.

Existing query interfaces for biological databases are either based on fixed forms or textual query languages. Users of a fixed form-based query interface are limited to performing some pre-defined queries providing a fixed view of the underlying database, while users of a free text query language-based interface have to understand the underlying data models, specific query languages and application schemas in order to formulate queries. Further, operations on application-specific complex data (e.g., DNA sequences, proteins), which are usually provided by a variety of software packages with their own format requirements and peculiarities, are not available as part of, nor integrated with biological query interfaces. In this paper, we describe generic tools that provide powerful and flexible support for interactively exploring biological databases in a uniform and consistent way, that is via common data models, formats, and notations, in the framework of the Object-Protocol Model (OPM). These tools include (i) a Java graphical query construction tool with support for automatic generation of Web query forms that can be either used for further specifying conditions, or can be saved and customized; (ii) query processors for interpreting and executing queries that may involve complex application-specific objects, and that could span multiple heterogeneous databases and file systems; and (iii) utilities for automatic generation of HTML pages containing query results, that can be browsed using a Web browser. These tools avoid the restrictions imposed by traditional fixed-form query interfaces, while providing users with simple and intuitive facilities for formulating ad-hoc queries across heterogeneous databases, without the need to understand the underlying data models and query languages.

Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome.

Single-nucleotide polymorphisms (SNPs) are the most frequent type of variation in the human genome, and they provide powerful tools for a variety of medical genetic studies. In a large-scale survey for SNPs, 2.3 megabases of human genomic DNA was examined by a combination of gel-based sequencing and high-density variation-detection DNA chips. A total of 3241 candidate SNPs were identified. A genetic map was constructed showing the location of 2227 of these SNPs. Prototype genotyping chips were developed that allow simultaneous genotyping of 500 SNPs. The results provide a characterization of human diversity at the nucleotide level and demonstrate the feasibility of large-scale identification of human SNPs.