Simplifying Gene Expression Microarray Comparative Analysis (original) (raw)
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Background: In the last decade, a large amount of microarray gene expression data has been accumulated in public repositories. Integrating and analyzing high-throughput gene expression data have become key activities for exploring gene functions, gene networks and biological pathways. Effectively utilizing these invaluable microarray data remains challenging due to a lack of powerful tools to integrate large-scale gene-expression information across diverse experiments and to search and visualize a large number of gene-expression data points. Results: Gene Expression Browser is a microarray data integration, management and processing system with webbased search and visualization functions. An innovative method has been developed to define a treatment over a control for every microarray experiment to standardize and make microarray data from different experiments homogeneous. In the browser, data are pre-processed offline and the resulting data points are visualized online with a 2-layer dynamic web display. Users can view all treatments over control that affect the expression of a selected gene via Gene View, and view all genes that change in a selected treatment over control via treatment over control View. Users can also check the changes of expression profiles of a set of either the treatments over control or genes via Slide View. In addition, the relationships between genes and treatments over control are computed according to gene expression ratio and are shown as co-responsive genes and co-regulation treatments over control.
Genetics and Molecular Biology
There are still numerous challenges to be overcome in microarray data analysis because advanced, state-of-the-art analyses are restricted to programming users. Here we present the Gene Expression Analysis Platform, a versatile, customizable, optimized, and portable software developed for microarray analysis. GEAP was developed in C# for the graphical user interface, data querying, storage, results filtering and dynamic plotting, and R for data processing, quality analysis, and differential expression. Through a new automated system that identifies microarray file formats, retrieves contents, detects file corruption, and solves dependencies, GEAP deals with datasets independently of platform. GEAP covers 32 statistical options, supports quality assessment, differential expression from single and dual-channel experiments, and gene ontology. Users can explore results by different plots and filtering options. Finally, the entire data can be saved and organized through storage features, optimized for memory and data retrieval, with faster performance than R. These features, along with other new options, are not yet present in any microarray analysis software. GEAP accomplishes data analysis in a faster, straightforward, and friendlier way than other similar software, while keeping the flexibility for sophisticated procedures. By developing optimizations, unique customizations and new features, GEAP is destined for both advanced and non-programming users.
DSGeo: Software tools for cross-platform analysis of gene expression data in GEO
2010
The Gene Expression Omnibus (GEO) is the largest resource of public gene expression data. While GEO enables data browsing, query and retrieval, additional tools can help realize its potential for aggregating and comparing data across multiple studies and platforms. This paper describes DSGeo -a collection of valuable tools that were developed for annotating, aggregating, integrating and analyzing data deposited in GEO. The core set of tools include a Relational Database, a Data Loader, a Data Browser and an Expression Combiner and Analyzer.
BMC Research Notes, 2011
Background: Although, systematic analysis of gene annotation is a powerful tool for interpreting gene expression data, it sometimes is blurred by incomplete gene annotation, missing expression response of key genes and secondary gene expression responses. These shortcomings may be partially circumvented by instead matching gene expression signatures to signatures of other experiments. Findings: To facilitate this we present the Functional Association Response by Overlap (FARO) server, that match input signatures to a compendium of 242 gene expression signatures, extracted from more than 1700 Arabidopsis microarray experiments.
Bioinformatics, 2002
Summary: SNOMAD is a collection of algorithms for the normalization and standardization of gene expression datasets derived from diverse biological and technological sources. In addition to conventional transformations and visualization tools, SNOMAD includes two non-linear transformations which correct for bias and variance which are non-uniformly distributed across the range of microarray element signal intensities: (1) Local mean normalization; and (2) Local variance correction (Z-score generation using a locally calculated standard deviation). Availability: The SNOMAD tools were developed in the R statistical language http://www.r-project.org/. SNOMAD is an interactive, user-friendly web-application which can be accessed freely via the internet with any standard HTML browser: http://pevsnerlab.kennedykrieger.org/snomad.htm. Contact: ccolantu@jhmi.edu or pevsner@jhmi.edu * To whom correspondence should be addressed. † Present address: Wadham College, Oxford University, UK.
GEPAS: a web-based resource for microarray gene expression data analysis
Nucleic Acids Research, 2003
We present a web-based pipeline for microarray gene expression profile analysis, GEPAS, which stands for Gene Expression Profile Analysis Suite (http://gepas.bioinfo.cnio.es). GEPAS is composed of different interconnected modules which include tools for data pre-processing, two-conditions comparison, unsupervised and supervised clustering (which include some of the most popular methods as well as home made algorithms) and several tests for differential gene expression among different classes, continuous variables or survival analysis. A multiple purpose tool for data mining, based on Gene Ontology, is also linked to the tools, which constitutes a very convenient way of analysing clustering results. On-line tutorials are available from our main web server
Gene Expression Atlas at the European Bioinformatics Institute
2010
The Gene Expression Atlas (http://www.ebi.ac .uk/gxa) is an added-value database providing information about gene expression in different cell types, organism parts, developmental stages, disease states, sample treatments and other biological/ experimental conditions. The content of this database derives from curation, re-annotation and statistical analysis of selected data from the ArrayExpress Archive of Functional Genomics Data. A simple interface allows the user to query for differential gene expression either (i) by gene names or attributes such as Gene Ontology terms, or (ii) by biological conditions, e.g. diseases, organism parts or cell types. The gene queries return the conditions where expression has been reported, while condition queries return which genes are reported to be expressed in these conditions. A combination of both query types is possible. The query results are ranked using various statistical measures and by how many independent studies in the database show the particular gene-condition association. Currently, the database contains information about more than 200 000 genes from nine species and almost 4500 biological conditions studied in over 30 000 assays from over 1000 independent studies.
GeneX: An Open Source gene expression database and integrated tool set
IBM Systems Journal, 2001
Because gene expression profiles are highly sensitive to sample and processing conditions, it is crucial to accurately represent these conditions along with the numeric data in a way that allows the conditions to be part of a query. The GeneX TM project is intended to provide an Open Source database and integrated tool set that will allow researchers to store and evaluate their gene expression data and, moreover, such evaluation will be independent of the technology used to obtain the data.