Dataset Information

SPIEDw: a searchable platform-independent expression database web tool.

ABSTRACT: BACKGROUND: SPIEDw is a web tool designed to facilitate fast and simple quantitative querying of publically available gene expression data. The resource is motivated by the observation that transcriptional profiles can serve as effective means of comparing biological states across a wide set of experiments. RESULTS: Gene expression data for over 200,000 experiments across multiple species and platforms have been collected into a searchable database. The new resource is a development of the previously published SPIED, which was designed to be downloaded and queried locally with SPIED software. SPIEDw features three significant improvements over the original version. Firstly, the number of experiments covered has been doubled and now includes Agilent and Illumina technologies. Secondly, SPIEDw has an enhanced search algorithm for speedy web-based querying and lastly an abridged dataset comprising the most regulated genes has been included for a speedier search and searching for enrichment of gene sets. CONCLUSIONS: SPIEDw is simple to use, not requiring any expertise in microarray analysis, and the output straightforward to interpret. It is hoped that this will open up gene expression data mining to the wider research community.

SUBMITTER: Williams G

PROVIDER: S-EPMC4046673 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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SPIEDw: a searchable platform-independent expression database web tool.

Williams Gareth G

BMC genomics 20131107

<h4>Background</h4>SPIEDw is a web tool designed to facilitate fast and simple quantitative querying of publically available gene expression data. The resource is motivated by the observation that transcriptional profiles can serve as effective means of comparing biological states across a wide set of experiments.<h4>Results</h4>Gene expression data for over 200,000 experiments across multiple species and platforms have been collected into a searchable database. The new resource is a development ...[more]

PMID: 24199845

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Project description:BackgroundThe retina is a multi-layered sensory tissue that lines the back of the eye and acts at the interface of input light and visual perception. Its main function is to capture photons and convert them into electrical impulses that travel along the optic nerve to the brain where they are turned into images. It consists of neurons, nourishing blood vessels and different cell types, of which neural cells predominate. Defects in any of these cells can lead to a variety of retinal diseases, including age-related macular degeneration, retinitis pigmentosa, Leber congenital amaurosis and glaucoma. Recent progress in genomics and microarray technology provides extensive opportunities to examine alterations in retinal gene expression profiles during development and diseases. However, there is no specific database that deals with retinal gene expression profiling. In this context we have built RETINOBASE, a dedicated microarray database for retina.DescriptionRETINOBASE is a microarray relational database, analysis and visualization system that allows simple yet powerful queries to retrieve information about gene expression in retina. It provides access to gene expression meta-data and offers significant insights into gene networks in retina, resulting in better hypothesis framing for biological problems that can subsequently be tested in the laboratory. Public and proprietary data are automatically analyzed with 3 distinct methods, RMA, dChip and MAS5, then clustered using 2 different K-means and 1 mixture models method. Thus, RETINOBASE provides a framework to compare these methods and to optimize the retinal data analysis. RETINOBASE has three different modules, "Gene Information", "Raw Data System Analysis" and "Fold change system Analysis" that are interconnected in a relational schema, allowing efficient retrieval and cross comparison of data. Currently, RETINOBASE contains datasets from 28 different microarray experiments performed in 5 different model systems: drosophila, zebrafish, rat, mouse and human. The database is supported by a platform that is designed to easily integrate new functionalities and is also frequently updated.ConclusionThe results obtained from various biological scenarios can be visualized, compared and downloaded. The results of a case study are presented that highlight the utility of RETINOBASE. Overall, RETINOBASE provides efficient access to the global expression profiling of retinal genes from different organisms under various conditions.

Project description:BackgroundConcurrent with the efforts currently underway in mapping microbial genomes using high-throughput sequencing methods, systems biologists are building metabolic models to characterize and predict cell metabolisms. One of the key steps in building a metabolic model is using multiple databases to collect and assemble essential information about genome-annotations and the architecture of the metabolic network for a specific organism. To speed up metabolic model development for a large number of microorganisms, we need a user-friendly platform to construct metabolic networks and to perform constraint-based flux balance analysis based on genome databases and experimental results.ResultsWe have developed a semi-automatic, web-based platform (MicrobesFlux) for generating and reconstructing metabolic models for annotated microorganisms. MicrobesFlux is able to automatically download the metabolic network (including enzymatic reactions and metabolites) of ~1,200 species from the KEGG database (Kyoto Encyclopedia of Genes and Genomes) and then convert it to a metabolic model draft. The platform also provides diverse customized tools, such as gene knockouts and the introduction of heterologous pathways, for users to reconstruct the model network. The reconstructed metabolic network can be formulated to a constraint-based flux model to predict and analyze the carbon fluxes in microbial metabolisms. The simulation results can be exported in the SBML format (The Systems Biology Markup Language). Furthermore, we also demonstrated the platform functionalities by developing an FBA model (including 229 reactions) for a recent annotated bioethanol producer, Thermoanaerobacter sp. strain X514, to predict its biomass growth and ethanol production.ConclusionMicrobesFlux is an installation-free and open-source platform that enables biologists without prior programming knowledge to develop metabolic models for annotated microorganisms in the KEGG database. Our system facilitates users to reconstruct metabolic networks of organisms based on experimental information. Through human-computer interaction, MicrobesFlux provides users with reasonable predictions of microbial metabolism via flux balance analysis. This prototype platform can be a springboard for advanced and broad-scope modeling of complex biological systems by integrating other "omics" data or 13 C- metabolic flux analysis results. MicrobesFlux is available at http://tanglab.engineering.wustl.edu/static/MicrobesFlux.html and will be continuously improved based on feedback from users.

Dataset Information

SPIEDw: a searchable platform-independent expression database web tool.

Publications

SPIEDw: a searchable platform-independent expression database web tool.

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