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Nucleic Acids Res
2007 Jul 01;35Web Server issue:W75-80. doi: 10.1093/nar/gkm229.
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Asterias: integrated analysis of expression and aCGH data using an open-source, web-based, parallelized software suite.
Díaz-Uriarte R
,
Alibés A
,
Morrissey ER
,
Cañada A
,
Rueda OM
,
Neves ML
.
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Asterias (http://www.asterias.info) is an open-source, web-based, suite for the analysis of gene expression and aCGH data. Asterias implements validated statistical methods, and most of the applications use parallel computing, which permits taking advantage of multicore CPUs and computing clusters. Access to, and further analysis of, additional biological information and annotations (PubMed references, Gene Ontology terms, KEGG and Reactome pathways) are available either for individual genes (from clickable links in tables and figures) or sets of genes. These applications cover from array normalization to imputation and preprocessing, differential gene expression analysis, class and survival prediction and aCGH analysis. The source code is available, allowing for extention and reuse of the software. The links and analysis of additional functional information, parallelization of computation and open-source availability of the code make Asterias a unique suite that can exploit features specific to web-based environments.
Figure 1. Asterias: functionality and data and information flow between sets of applications (see details in Figure 2). References for ADaCGH methods are: circular binary segmentation (21), wavelet-based smoothing (22), SW-ARRAY (23) and ACE (24). The method implemented in SignS is from (25).
Figure 2. Asterias: input/output and data and information flow between applications. Black and blue arrows involve files, green arrows URLs. Olive boxes denote graphical output.
Al-Shahrour,
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Al-Shahrour,
Discovering molecular functions significantly related to phenotypes by combining gene expression data and biological information.
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Al-Shahrour,
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Alibés,
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Ambroise,
Selection bias in gene extraction on the basis of microarray gene-expression data.
2002,
Pubmed
Baxter,
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2006,
Pubmed
Dave,
Prediction of survival in follicular lymphoma based on molecular features of tumor-infiltrating immune cells.
2004,
Pubmed
Dudoit,
Open source software for the analysis of microarray data.
2003,
Pubmed
Díaz-Uriarte,
Gene selection and classification of microarray data using random forest.
2006,
Pubmed
Herrero,
GEPAS: A web-based resource for microarray gene expression data analysis.
2003,
Pubmed
Herrero,
New challenges in gene expression data analysis and the extended GEPAS.
2004,
Pubmed
Hsu,
Denoising array-based comparative genomic hybridization data using wavelets.
2005,
Pubmed
Hyatt,
Gene expression microarrays: glimpses of the immunological genome.
2006,
Pubmed
Lai,
Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data.
2005,
Pubmed
Lingjaerde,
CGH-Explorer: a program for analysis of array-CGH data.
2005,
Pubmed
Montaner,
Next station in microarray data analysis: GEPAS.
2006,
Pubmed
Olshen,
Circular binary segmentation for the analysis of array-based DNA copy number data.
2004,
Pubmed
Pan,
Effects of threshold choice on biological conclusions reached during analysis of gene expression by DNA microarrays.
2005,
Pubmed
Potter,
Epidemiology, cancer genetics and microarrays: making correct inferences, using appropriate designs.
2003,
Pubmed
Price,
SW-ARRAY: a dynamic programming solution for the identification of copy-number changes in genomic DNA using array comparative genome hybridization data.
2005,
Pubmed
Psarros,
RACE: Remote Analysis Computation for gene Expression data.
2005,
Pubmed
Rainer,
CARMAweb: comprehensive R- and bioconductor-based web service for microarray data analysis.
2006,
Pubmed
Reiner,
Identifying differentially expressed genes using false discovery rate controlling procedures.
2003,
Pubmed
Rhodes,
Integrative analysis of the cancer transcriptome.
2005,
Pubmed
Romualdi,
MIDAW: a web tool for statistical analysis of microarray data.
2005,
Pubmed
Simon,
Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification.
2003,
Pubmed
Smyth,
Linear models and empirical bayes methods for assessing differential expression in microarray experiments.
2004,
Pubmed
Somorjai,
Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions.
2003,
Pubmed
Vaquerizas,
GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data.
2005,
Pubmed
Vaquerizas,
DNMAD: web-based diagnosis and normalization for microarray data.
2004,
Pubmed
Varma,
Bias in error estimation when using cross-validation for model selection.
2006,
Pubmed
Willenbrock,
A comparison study: applying segmentation to array CGH data for downstream analyses.
2005,
Pubmed
