MOTIVATION: The IntAct repository is one of the largest and most widely used databases for the curation and storage of molecular interaction data. These datasets need to be analyzed by computational methods. Software packages in the statistical environment R provide powerful tools for conducting such analyses. RESULTS: We introduce Rintact, a Bioconductor package that allows users to transform PSI-MI XML2.5 interaction data files from IntAct into R graph objects. On these, they can use methods from R and Bioconductor for a variety of tasks: determining cohesive subgraphs, computing summary statistics, fitting mathematical models to the data or rendering graphical layouts. Rintact provides a programmatic interface to the IntAct repository and allows the use of the analytic methods provided by R and Bioconductor. AVAILABILITY: Rintact is freely available at http://bioconductor.org

Biologists are increasingly relying on online biological databases to conduct their research. Despite the availability of hundreds of such databases, only a handful of them are effectively deployed by the community. The main reason for this is the lack of metadata provided by the databases, such as, the types of data stored, its coverage, and its relationship with other databases. Nevertheless, each database has a potential for supporting existing research investigations. The opportunity to leverage the wealth of data available presents several challenges related to data integration and representation. In this paper, we present BioFacets, a new integration system for Web based biological databases. The main feature of this system is the proposal of a new faceted classification system that allows for dynamic categorization of biological result sets. Other features of the system include query optimization, cache management and support for a meta-search engine that allows the system to efficiently search across multiple online biological databases

Integrating life science Web databases, while important and necessary, is a challenge for current integration systems mainly due to the large number of these databases, their heterogeneity and the fact that their interfaces may change often. BACIIS, a biological and chemical information integration system, is a tightly coupled federated database system that uses the mediator wrapper method in order to retrieve information from several remote Web databases. BACIIS relies on a semi-automated approach for generating and maintaining wrappers in order to provide a scalable system with a limited maintenance overhead. The semi-automatic wrapper induction in BACIIS is efficient because it is based on, but not limited to a domain knowledge. Tests show that the use of ontology increases the accuracy of the wrapper induction. We also present how the wrapper induction system facilitates wrapper update, and assists in the information extraction. By using a wrapper induction system for creation and maintenance of wrappers, scalability, flexibility, and stability of the integrated information system is easily maintained.

Life science Web databases are becoming increasingly essential in conducting everyday biological research. With more than 300 life science Web databases and the growing size of the life science data, searching and managing these complex data requires technology beyond that of traditional database systems. The open research issues include the interoperability of geographically distributed autonomous databases, which are generally only Web-accessible, and the seamless semantic-based integration of these databases with total transparency to the user. In this paper, the implementation of a Biological and Chemical Information Integration System (BACIIS) is presented. BACIIS supports the integration of multiple heterogeneous life science Web databases and allows the execution of global applications that extend beyond the boundaries of individual databases. This paper discusses the architecture of BACIIS. It also discusses the techniques used to extract and integrate data from the various life science Web databases.

The incompatibilities among complex data formats and various schema used by biological databases that house these data are becoming a bottleneck in biological research. For example, biological data format varies from simple words (e.g. gene name), numbers (e.g. molecular weight) to sequence strings (e.g. nucleic acid sequence), to even more complex data formats such as taxonomy trees. Some information is embedded in narrative text, such as expert comments and publications. Some other information is expressed as graphs or images (e.g. pathways networks). The confederation of heterogeneous web databases has become a crucial issue in today's biological research. In other words, interoperability has to be archieved among the biological web databases and the heterogeneity of the web databases has to be resolved. This paper presents a biological ontology, BAO, and discusses its advantages in supporting the semantic integration of biological web databases are discussed.

Recent advances in the development of new, high throughput screening technologies have resulted in a wealth of life science data. The complexity and volume of these data require technology beyond that of traditional database systems. Open research issues include the seamless, semantic-based integration of geographically distributed, autonomous web databases. Biological And Chemical Integration Information System (BACIIS) addresses these issues. This paper discusses the challenges associated with the integration of life science web databases and the techniques used in BACIIS to address these challenges. Particularly, the execution of life science, multidatabase queries is investigated, and an approach that can overcome the limitations of the query capabilities of each individual life science web database participating in the integration is presented. This approach hides the heterogeneity of the individual databases from the user while providing an efficient execution plan for complex, life science, multidatabase queries.