Minimum information about a microarray experiment (MIAME)-toward standards for microarray data.

Microarray analysis has become a widely used tool for the generation of gene expression data on a genomic scale. Although many significant results have been derived from microarray studies, one limitation has been the lack of standards for presenting and exchanging such data. Here we present a proposal, the Minimum Information About a Microarray Experiment (MIAME), that describes the minimum information required to ensure that microarray data can be easily interpreted and that results derived from its analysis can be independently verified. The ultimate goal of this work is to establish a standard for recording and reporting microarray-based gene expression data, which will in turn facilitate the establishment of databases and public repositories and enable the development of data analysis tools. With respect to MIAME, we concentrate on defining the content and structure of the necessary information rather than the technical format for capturing it.

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.

Facilities for exploring molecular biology databases on the Web: a comparative study.

We discuss criteria for evaluating and comparing the main facilities provided by molecular biology databases (MBDs) for exploring (that is, retrieving and interpreting data) on the Web. We use these criteria for examining the facilities supported by typical MBDs such as Genbank, AtDB, GSDB, GDB, and MGD (as of September 5, 1996).

Characterizing heterogeneous molecular biology database systems.

Molecular biology data are distributed among multiple databases. Although containing related data, these databases are often isolated and are characterized by various degrees of heterogeneity: they usually represent different views (schemas) of the scientific domain and are implemented using different data management systems. Currently, several systems support managing data in heterogeneous molecular biology databases. Lack of clear criteria for characterizing such systems precludes comprehensive evaluations of these systems or determining their relationships in terms of shared goals and facilities. In this paper, we propose criteria that would facilitate characterizing, evaluating, and comparing heterogeneous molecular biology database systems.