MOTIVATION: To identify and characterize regions of functional interest in genomic sequence requires full, flexible query access to an integrated, up-to-date view of all related information, irrespective of where it is stored (within an organization or across the Internet) and its format (traditional database, flat file, web site, results of runtime analysis). Wide-ranging multi-source queries often return unmanageably large result sets, requiring non-traditional approaches to exclude extraneous data. RESULTS: Target Informatics Net (TINet) is a readily extensible data integration system developed at GlaxoSmith- Kline (GSK), based on the Object-Protocol Model (OPM) multidatabase middleware system of Gene Logic Inc. Data sources currently integrated include: the Mouse Genome Database (MGD) and Gene Expression Database (GXD), GenBank, SwissProt, PubMed, GeneCards, the results of runtime BLAST and PROSITE searches, and GSK proprietary relational databases. Special-purpose class methods used to filter and augment query results include regular expression pattern-matching over BLAST HSP alignments and retrieving partial sequences derived from primary structure annotations. All data sources and methods are accessible through an SQL-like query language or a GUI, so that when new investigations arise no additional programming beyond query specification is required. The power and flexibility of this approach are illustrated in such integrated queries as: (1) 'find homologs in genomic sequence to all novel genes cloned and reported in the scientific literature within the past three months that are linked to the MeSH term 'neoplasms"; (2) 'using a neuropeptide precursor query sequence, return only HSPs where the target genomic sequences conserve the G[KR][KR] motif at the appropriate points in the HSP alignment'; and (3) 'of the human genomic sequences annotated with exon boundaries in GenBank, return only those with valid putative donor/acceptor sites and start/stop codons'.

Summary form only given. The Object-Protocol Model (OPM) data management tools provide facilities for rapid development, documentation, and flexible exploration of scientific databases. The tools are based on OPM, an object-oriented data model which is similar to the ODMG standard, but also supports extensions for modeling scientific data. Databases designed using OPM can be implemented using a variety of commercial relational DBMSs, using schema translation tools that generate complete DBMS database definitions from OPM schemas. Further, OPM schemas can be retrofitted on top of existing databases defined using a variety of notations, such as the relational data model or the ASN.1 data exchange format, using OPM retrofitting tools. Several archival molecular biology databases have been designed and implemented using the OPM tools, including the Genome Database (GDB) and the Protein Data Bank (PDB), while other scientific databases, such as the Genome Sequence Database (GSDB), have been retrofitted with semantically enhanced views using the OPM tools.

The need to transform data between heterogeneous databases arises from a number of critical tasks in data management. These tasks are complicated by schema evolution in the underlying databases and by the presence of non-standard database constraints. We describe a declarative language called WOL (Well-founded Object Logic) for specifying such transformations, and its implementation in a system called Morphase (an "enzyme" for morphing data). WOL is designed to allow transformations between the complex data structures which arise in object-oriented databases as well as in complex relational databases, and to allow for reasoning about the interactions between database transformations and constraints.

The Object-Protocol Model (OPM) data management tools provide facilities for rapid development, documentation, and flexible exploration of scientific databases. The tools are based on OPM, an object oriented data model which is similar to the ODMG standard, but also supports extensions for modeling scientific data (L.A. Chen and V.M. Markowitz, 1995). Databases designed using OPM can be implemented using a variety of commercial relational DBMSs, using schema translation tools that generate complete DBMS database definitions from OPM schemas (L.A. Chen and V.M. Markowitz, 1996). Further OPM schemas can be retrofitted on top of existing databases defined using a variety of notations, such as the relational data model or the ASN.1 data exchange format, using OPM retrofitting tools (L.A. Chen et al., 1997).

Scientific databases (ScDBs) are used to archive and retrieve data describing objects of scientific inquiry. Since these ScDBs must provide continuous and efficient access to large communities of scientists, they are often developed with reliable commercial relational database management systems (DBMSs) or file systems. However, relational DBMSs and flat files do not provide constructs for representing directly ScDB-specific objects and experimental procedures, and therefore they are often hard to develop, maintain, and explore. In this paper, we present a retrofitting tool for constructing and maintaining ScDB views using an object-oriented data model, and describe our experience with retrofitting ScDBs that have been originally developed using relational DBMSs and file systems. The retrofitting tool is part of a data management toolkit based on the Object-Protocol Model (OPM). The OPM toolkit provides facilities for developing databases defined using OPM and for querying and browsing...

Human Genome Project databases present a confluence of interesting database challenges: rapid schema and data evolution, complex data entry and constraint management, and the need to integrate multiple data sources and software systems which range over a wide variety of models and formats. While these challenges are not necessarily unique to biological databases, their combination, intensity and complexity are unusual and make automated solutions imperative. We illustrate these problems in the context of the Philadelphia Genome Center for Human Chromosome 22, and describe a new approach to a solution for these problems, by means of a deductive language for expressing database transformations and constraints.