The Integrated Microbial Genomes (IMG) system integrates microbial community aggregate genomes (metagenomes) with genomes from all domains of life. IMG provides tools for analyzing and reviewing the structural and functional annotations of metagenomes and genomes in a comparative context. At the core of the IMG system is a data warehouse that contains genome and metagenome datasets provided by scientific users, as well as public bacterial, archaeal, eukaryotic, and viral genomes from the US National Center for Biotechnology Information genomic archive and a rich set of engineered, environmental and host associated metagenomes. Genomes and metagenome datasets are processed using IMG's microbial genome and metagenome sequence data processing pipelines and then are integrated into the data warehouse using IMG's data integration toolkit. Microbial genome and metagenome application specific user interfaces provide access to different subsets of IMG's data and analysis toolkits. Genome and metagenome analysis is a gene centric iterative process that involves a sequence (composition) of data exploration and comparative analysis operations, with individual operations expected to have rapid response time.

MOTIVATION: A rapidly increasing number of microbial genomes are sequenced by organizations worldwide and are eventually included into various public genome data resources. The quality of the annotations depends largely on the original dataset providers, with erroneous or incomplete annotations often carried over into the public resources and difficult to correct. RESULTS: We have developed an Expert Review (ER) version of the Integrated Microbial Genomes (IMG) system, with the goal of supporting systematic and efficient revision of microbial genome annotations. IMG ER provides tools for the review and curation of annotations of both new and publicly available microbial genomes within IMG's rich integrated genome framework. New genome datasets are included into IMG ER prior to their public release either with their native annotations or with annotations generated by IMG ER's annotation pipeline. IMG ER tools allow addressing annotation problems detected with IMG's comparative analysis tools, such as genes missed by gene prediction pipelines or genes without an associated function. Over the past year, IMG ER was used for improving the annotations of about 150 microbial genomes.

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 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...

Research has been done in discovering rules from databases and in applying these rules to intensional answers to database queries, semantic query optimization, etc. However, rules discovered by one group of users may not be used by other groups of users in their applications due to certain mismatches. In this paper, we address the problems of using discovered rules for query answering, and then propose algorithms for rewriting, applying and maintaining these rules.

We examine the main requirements for modeling scientific experiments and propose constructs that fulfil these requirements. We show that existing object-oriented and semantic data models do not provide such constructs. Experiment (protocol) and object constructs can be combined in order to provide seamless object and experiment modeling. We present an example of combining protocol and object constructs into a unified framework, the Object-Protocol Model (OPM), and briefly describe the implementation of an OPM interface on top of commercial relational database management systems (DBMSs).>

A novel execution model for rule application in active databases is developed and applied to the problem of updating derived data in a database represented using a semantic, object-based database model. The execution model is based on the use of “limited ambiguity rules” (LARs), which permit disjunction in rule actions. The execution model essentially performs a breadth-first exploration of alternative extensions of a user-requested update. Given an object-based database schema, both integrity constraints and specifications of derived classes and attributes are compiled into a family of limited ambiguity rules. A theoretical analysis shows that the approach is sound: the execution model returns all valid “completions” of a user-requested update, or terminates with an appropriate error notification. The complexity of the approach in connection with derived data update is considered.