@article {20478823,
	title = {Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling.},
	journal = {Nucleic Acids Research},
	volume = {38},
	year = {2010},
	month = {2010 May 16},
	pages = {W210-W213. Featured in NAR},
	chapter = {Featured in NAR},
	abstract = {<p>Babelomics is a response to the growing necessity of integrating and analyzing different types of genomic data in an environment that allows an easy functional interpretation of the results. Babelomics includes a complete suite of methods for the analysis of gene expression data that include normalization (covering most commercial platforms), pre-processing, differential gene expression (case-controls, multiclass, survival or continuous values), predictors, clustering; large-scale genotyping assays (case controls and TDTs, and allows population stratification analysis and correction). All these genomic data analysis facilities are integrated and connected to multiple options for the functional interpretation of the experiments. Different methods of functional enrichment or gene set enrichment can be used to understand the functional basis of the experiment analyzed. Many sources of biological information, which include functional (GO, KEGG, Biocarta, Reactome, etc.), regulatory (Transfac, Jaspar, ORegAnno, miRNAs, etc.), text-mining or protein-protein interaction modules can be used for this purpose. Finally a tool for the de novo functional annotation of sequences has been included in the system. This provides support for the functional analysis of non-model species. Mirrors of Babelomics or command line execution of their individual components are now possible. Babelomics is available at http://www.babelomics.org.</p>},
	keywords = {babelomics, gene expression, genotyping, gepas, GSA, GWAS},
	url = {http://nar.oxfordjournals.org/content/38/suppl_2/W210.full},
	author = {Medina, Ignacio and Carbonell, Jos{\'e} and Pulido, Luis and Madeira, Sara C and Goetz, Stefan and Ana Conesa and T{\'a}rraga, Joaqu{\'\i}n and Pascual-Montano, Alberto and Nogales-Cadenas, Ruben and Santoyo, Javier and Garc{\'\i}a, Francisco and Marb{\`a}, Martina and Montaner, David and Joaqu{\'\i}n Dopazo}
}
@article {IgnacioMedina07012009,
	title = {Gene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies},
	journal = {Nucl. Acids Res.},
	volume = {37},
	number = {suppl_2},
	year = {2009},
	pages = {W340-344},
	abstract = {<p>Genome-wide association studies have become a popular strategy to find associations of genes to traits of interest. Despite the high-resolution available today to carry out genotyping studies, the success of its application in real studies has been limited by the testing strategy used. As an alternative to brute force solutions involving the use of very large cohorts, we propose the use of the Gene Set Analysis (GSA), a different analysis strategy based on testing the association of modules of functionally related genes. We show here how the Gene Set-based Analysis of Polymorphisms (GeSBAP), which is a simple implementation of the GSA strategy for the analysis of genome-wide association studies, provides a significant increase in the power testing for this type of studies. GeSBAP is freely available at http://bioinfo.cipf.es/gesbap/</p>},
	keywords = {babelomics, gene set, GESBAP, pathway-based analysis, SNP},
	doi = {10.1093/nar/gkp481},
	url = {http://nar.oxfordjournals.org/cgi/content/abstract/37/suppl_2/W340},
	author = {Medina, Ignacio and Montaner, David and Bonifaci, N{\'u}ria and Pujana, Miguel Angel and Carbonell, Jos{\'e} and T{\'a}rraga, Joaqu{\'\i}n and Fatima Al-Shahrour and Dopazo, Joaquin}
}
