BarleyExpress: a web-based submission tool for enriched microarray database annotations

BarleyExpress is a web-based microarray experiment data submission tool for BarleyBase, a public data resource of Affymetrix GeneChip data for plants. BarleyExpress uses the Plant Ontology vocabularies and enhances the MIAME guidelines to standardize the annotation of microarray gene expression experiments. In addition, BarleyExpress provides explicit support for factorial experiment design and template loading methods to ease the submission process for large experiments. AVAILABILITY: http://barleybase.org SUPPLEMENTARY INFORMATION: BarleyExpress Users Manual.     

ADAMIS: a database for medical information systems

This paper describes the design and implementation of ADAMIS ('A database for medical information systems'). ADAMIS is a relational database management system for a general hospital environment. Apart from the usual database (DB) facilities of data definition and data manipulation, ADAMIS supports a query language called the 'simplified medical query language' (SMQL) which is completely end-user oriented and highly non-procedural. Other features of ADAMIS include provision of facilities for statistics collection and report generation. ADAMIS also provides adequate security and integrity features and has been designed mainly for use on interactive terminals.     

StressDB: a locally installable web-based relational microarray database designed for small user communities

We have built a microarray database, StressDB, for management of microarray data from our studies on stress-modulated genes in Arabidopsis. StressDB provides small user groups with a locally installable web-based relational microarray database. It has a simple and intuitive architecture and has been designed for cDNA microarray technology users. StressDB uses Windows(trade mark) 2000 as the centralized database server with Oracle(trade mark) 8i as the relational database management system. It allows users to manage microarray data and data-related biological information over the Internet using a web browser. The source-code is currently available on request from the authors and will soon be made freely available for downloading from our website athttp://arastressdb.cac.psu.edu.     

Comparison of different microarray data analysis programs and description of a database for microarray data management

Data analysis and management represent a major challenge for gene expression studies using microarrays. Here, we compare different methods of analysis and demonstrate the utility of a personal microarray database. Gene expression during HIV infection of cell lines was studied using Affymetrix U-133 A and B chips. The data were analyzed using Affymetrix Microarray Suite and Data Mining Tool, Silicon Genetics GeneSpring, and dChip from Harvard School of Public Health. A small-scale database was established with FileMaker Pro Developer to manage and analyze the data. There was great variability among the programs in the lists of significantly changed genes constructed from the same data. Similarly choices of different parameters for normalization, comparison, and standardization greatly affected the outcome. As many probe sets on the U133 chip target the same Unigene clusters, the Unigene information can be used as an internal control to confirm and interpret the probe set results. Algorithms used for the determination of changes in gene expression require further refinement and standardization. The use of a personal database powered with Unigene information can enhance the analysis of gene expression data.     

RESOURCERER: a database for annotating and linking microarray resources within and across species

Microarray expression analysis is providing unprecedented data on gene expression in humans and mammalian model systems. Although such studies provide a tremendous resource for understanding human disease states, one of the significant challenges is cross-referencing the data derived from different species, across diverse expression analysis platforms, in order to properly derive inferences regarding gene expression and disease state. To address this problem, we have developed RESOURCERER, a microarray-resource annotation and cross-reference database built using the analysis of expressed sequence tags (ESTs) and gene sequences provided by the TIGR Gene Index (TGI) and TIGR Orthologous Gene Alignment (TOGA) databases [now called Eukaryotic Gene Orthologs (EGO)].     

LIMaS: the JAVA-based application and database for microarray experiment tracking

Microarrays allow monitoring of gene expression for tens of thousands of genes in parallel and are being used routinely to generate huge amounts of valuable data. Handling and analysis of such data are becoming major bottlenecks in the utilization of the technology. To enable the researcher to interpret the results postanalysis, we have developed a laboratory information management system for microarrays (LIMaS) with an n-tier Java front-end and relational database to record and manage large-scale expression data preanalysis. This system enables the laboratory to replace the paper trail with an efficient and fully customizable interface giving it the ability to adapt to any working practice, e.g., handling many resources used to form many products (chaining of resources). The ability to define sets of activities, resources, and workflows makes LIMaS MIAME-supportive.LIMaS is available for download at (http://www.mgu.har.mrc.ac.uk/microarray.)     

DRAGON View: information visualization for annotated microarray data

The DRAGON View information visualization tools aid in the comprehensive analysis of large-scale gene expression data that has been annotated with biologically relevant information through the generation of three types of complementary graphical outputs.     

BmuG@Sbase--a microarray database and analysis tool

The manufacture and use of a whole-genome microarray is a complex process and it is essential that all data surrounding the process is stored, is accessible and can be easily associated with the data generated following hybridization and scanning. As part of a program funded by the Wellcome Trust, the Bacterial Microarray Group at St. George's Hospital Medical School (BmuG@S) will generate whole-genome microarrays for 12 bacterial pathogens for use in collaboration with specialist research groups. BmuG@S will collaborate with these groups at all levels, including the experimental design, methodology and analysis. In addition, we will provide informatic support in the form of a database system (BmuG@Sbase). BmuG@Sbase will provide access through a web interface to the microarray design data and will allow individual users to store their data in a searchable, secure manner. Tools developed by BmuG@S in collaboration with specific research groups investigating analysis methodology will also be made available to those groups using the arrays and submitting data to BmuG@Sbase.     

CIBEX: center for information biology gene expression database

We describe the current status of the gene expression database CIBEX (Center for Information Biology gene EXpression database, http://cibex.nig.ac.jp), with a data retrieval system in compliance with MIAME, a standard that the MGED Society has developed for comparing and data produced in microarray experiments at different laboratories worldwide. CIBEX serves as a public repository for a wide range of high-throughput experimental data in gene expression research, including microarray-based experiments measuring mRNA, serial analysis of gene expression (SAGE tags), and mass spectrometry proteomic data.     

WiGID: wireless genome information database

WiGID, wireless genome information database, is a new application for mobile internet and can be reached through wireless application protocol (WAP). The main purpose of WiGID is to give easy access to information on completely sequenced genomes. Genome entries in WiGID can be queried by the number of open reading frames (ORFs), genus and species name and year published. Initial search results are linked to information on the full entry. AVAILABILITY: WiGID can be accessed through WAP at http://wigid.cgb.ki.se/index.wml and through the regular internet at http://wigid.cgb.ki.se.     

MAPS: a microarray project system for gene expression experiment information and data validation

SUMMARY: MAPS is a MicroArray Project System for management and interpretation of microarray gene expression experiment information and data. Microarray project information is organized to track experiments and results that are: (1) validated by performing analysis on stored replicate gene expression data; and (2) queried according to the biological classifications of genes deposited on microarray chips.     

DGEM--a microarray gene expression database for primary human disease tissues

Gene expression patterns can reflect gene regulations in human tissues under normal or pathologic conditions. Gene expression profiling data from studies of primary human disease samples are particularly valuable since these studies often span many years in order to collect patient clinical information and achieve a large sample size. Disease-to-Gene Expression Mapper (DGEM) provides a beneficial community resource to access and analyze these data; it currently includes Affymetrix oligonucleotide array datasets for more than 40 human diseases and 1400 samples. The data are normalized to the same scale and stored in a relational database. A statistical-analysis pipeline was implemented to identify genes abnormally expressed in disease tissues or genes whose expressions are associated with clinical parameters such as cancer patient survival. Data-mining results can be queried through a web-based interface at http://dgem.dhcp.iupui.edu/. The query tool enables dynamic generation of graphs and tables that are further linked to major gene and pathway resources that connect the data to relevant biology, including Entrez Gene and Kyoto Encyclopedia of Genes and Genomes (KEGG). In summary, DGEM provides scientists and physicians a valuable tool to study disease mechanisms, to discover potential disease biomarkers for diagnosis and prognosis, and to identify novel gene targets for drug discovery. The source code is freely available for non-profit use, on request to the authors.     

Storage and retrieval of microarray data and open source microarray database software

Microarray technology has been widely adopted by researchers who use both home-made microarrays and microarrays purchased from commercial vendors. Associated with the adoption of this technology has been a deluge of complex data, both from the microarrays themselves, and also in the form of associated meta data, such as gene annotation information, the properties and treatment of biological samples, and the data transformation and analysis steps taken downstream. In addition, standards for annotation and data exchange have been proposed, and are now being adopted by journals and funding agencies alike. The coupling of large quantities of complex data with extensive and complex standards require all but the most small-scale of microarray users to have access to a robust and scaleable database with various tools. In this review, we discuss some of the desirable properties of such a database, and look at the features of several freely available alternatives.