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The Medicago Genome Initiative (MGI) is a database of EST sequences of the model legume Medicago truncatula. The database is available to the public and has resulted from a collaborative research effort between the Samuel Roberts Noble Foundation and the National Center for Genome Resources to investigate the genome of M.truncatula. MGI is part of the greater integrated Medicago functional genomics program at the Noble Foundation (http://www.noble .org), which is taking a global approach in studying the genetic and biochemical events associated with the growth, development and environmental interactions of this model legume. Our approach will include: large-scale EST sequencing, gene expression profiling, the generation of M.truncatula activation-tagged and promoter trap insertion mutants, high-throughput metabolic profiling, and proteome studies. These multidisciplinary information pools will be interfaced with one another to provide scientists with an integrated, holistic set of tools to address fundamental questions pertaining to legume biology. The public interface to the MGI database can be accessed at http://www.ncgr.org/research/mgi.

The National Center for Genome Resources (NCGR) has developed a high-throughput DNA (deoxyribonucleic acid) sequence analysis pipeline, which allows researchers at remote sites to submit biological sequence information for rapid analysis, the results of which can be queried through a Web interface. Behind the browser interface is a relational database used to manage both the raw data and the results of the different analyses performed, and a server, which performs those analyses. The system allows multiple contributors to submit data and also allows the data to be marked as "private" or as available to the general public. The CPU-intensive part of the processing is done on a 40- processor domain of a Sun Enterprise 10000 computer, which is represented by a distributed system of software objects, implemented in CORBATM (Common Object Request Broker ArchitectureTM). In this paper we discuss the architecture of the pipeline, the database support, types of DNA sequence analysis used, the distributed analysis system, and the capabilities of the Web interface. As a case study, we present data from an ongoing collaborative project in which expressed sequence tags (ESTs) from Medicago truncatula are being processed. M. truncatula is a plant that is used as a research model for crops in the legume family, an economically important group of food and forage plants.

The Phytophthora Genome Initiative (PGI) is a distributed collaboration to study the genome and evolution of a particularly destructive group of plant pathogenic oomycete, with the goal of understanding the mechanisms of infection and resistance. NCGR provides informatics support for the collaboration as well as a centralized data repository. In the pilot phase of the project, several investigators prepared Phytophthora infestans and Phytophthora sojae EST and Phytophthora sojae BAC libraries and sent them to another laboratory for sequencing. Data from sequencing reactions were transferred to NCGR for analysis and curation. An analysis pipeline transforms raw data by performing simple analyses (i.e., vector removal and similarity searching) that are stored and can be retrieved by investigators using a web browser. Here we describe the database and access tools, provide an overview of the data therein and outline future plans. This resource has provided a unique opportunity for the distributed, collaborative study of a genus from which relatively little sequence data are available. Results may lead to insight into how better to control these pathogens. The homepage of PGI can be accessed at http:www.ncgr.org/pgi , with database access through the database access hyperlink.

The Genome Sequence DataBase (GSDB) is a database of publicly available nucleotide sequences and their associated biological and bibliographic information. Several notable changes have occurred in the past year: GSDB stopped accepting data submissions from researchers; ownership of data submitted to GSDB was transferred to GenBank; sequence analysis capabilities were expanded to include Smith-Waterman and Frame Search; and Sequence Viewer became available to Mac users. The content of GSDB remains up-to-date because publicly available data is acquired from the International Nucleotide Sequence Database Collaboration databases (IC) on a nightly basis. This allows GSDB to continue providing researchers with the ability to analyze, query and retrieve nucleotide sequences in the database. GSDB and its related tools are freely accessible from the URL: http://www.ncgr.org

During 1998 the primary focus of the Genome Sequence DataBase (GSDB; http://www.ncgr.org/gsdb ) located at the National Center for Genome Resources (NCGR) has been to improve data quality, improve data collections, and provide new methods and tools to access and analyze data. Data quality has been improved by extensive curation of certain data fields necessary for maintaining data collections and for using certain tools. Data quality has also been increased by improvements to the suite of programs that import data from the International Nucleotide Sequence Database Collaboration (IC). The Sequence Tag Alignment and Consensus Knowledgebase (STACK), a database of human expressed gene sequences developed by the South African National Bioinformatics Institute (SANBI), became available within the last year, allowing public access to this valuable resource of expressed sequences. Data access was improved by the addition of the Sequence Viewer, a platform-independent graphical viewer for GSDB sequence data. This tool has also been integrated with other searching and data retrieval tools. A BLAST homology search service was also made available, allowing researchers to search all of the data, including the unique data, that are available from GSDB. These improvements are designed to make GSDB more accessible to users, extend the rich searching capability already present in GSDB, and to facilitate the transition to an integrated system containing many different types of biological data.