Object-Protocol Model Research Articles

Web data retrieval and extraction

We present the Object-Web Mediator to querying integrated Web data sources composed of a retrieval component based on an intermediate object view mechanism and search views, and an XML engine. Search views map the source capabilities to attributes defined at object classes, and parsers that process retrieved documents and cache them in XML format. The XML engine queries cached documents, extracts data, and returns extracted data for evaluation. The originality of this approach consists of a generic view mechanism to access data sources with limited data access and complex capabilities, and an XML engine to support data extraction and reorganization. This approach has been developed and demonstrated as part of the multi-database system supporting queries via uniform Object Protocol Model interfaces against public Web data sources of interest to the biologists.

Biological data integration: wrapping data and tools.

Nowadays scientific data is inevitably digital and stored in a wide variety of formats in heterogeneous systems. Scientists need to access an integrated view of remote or local heterogeneous data sources with advanced data accessing, analyzing, and visualization tools. Building a digital library for scientific data requires accessing and manipulating data extracted from flat files or databases, documents retrieved from the Web as well as data generated by software. We present an approach to wrapping web data sources, databases, flat files, or data generated by tools through a database view mechanism. Generally, a wrapper has two tasks: it first sends a query to the source to retrieve data and, second builds the expected output with respect to the virtual structure. Our wrappers are composed of a retrieval component based on an intermediate object view mechanism called search views mapping the source capabilities to attributes, and an eXtensible Markup Language (XML) engine, respectively, to perform these two tasks. The originality of the approach consists of: 1) a generic view mechanism to access seamlessly data sources with limited capabilities and 2) the ability to wrap data sources as well as the useful specific tools they may provide. Our approach has been developed and demonstrated as part of the multidatabase system supporting queries via uniform object protocol model (OPM) interfaces.

Extending traditional query-based integration approaches for functional characterization of post-genomic data.

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. 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'.

An Object-Protocol Model Multidatabase Query System for Rice and Maize

With the recent explosion in genome analysis of various organisms, access to different databases often involves browsing through repositories characterized by various degrees of heterogeneity. In most cases, each database is characterized by a specific view of the domain and implemented using a specific database management system. The Rice Genome Research Program (RGP) and the Missouri Maize Project (MMP) have developed a scheme for cross querying of rice and maize databases using the OPM (Object-Protocol Model) tools of Gene Logic, Inc. This enables users to explore semantics and to assemble biological information from the two databases. As rice is considered a model for genome analysis of cereal crops, genome information of rice can be directly reflected in maize and vice-versa.

An overview of the Object Protocol Model (OPM) and the OPM data management tools

In this paper, we overview the Object-Protocol Model (OPM) and a suite of data management tools based on OPM. OPM is a data model that allows specifying database structures and queries in terms of objects and protocols specific to scientific (e.g., molecular biology laboratory) applications. Thus, scientific experiments and their resources can be described using OPM in a unified way. OPM data management tools provide facilities for specifying and querying relational databases in terms of OPM constructs, and automatically generate database specifications and queries for implementing OPM on top of commercial relational database management systems (DBMSs). OPM tools increase the efficiency of developing scientific databases using relational DBMSs, while insulating scientists from the underlying DBMSs.