@article {15840702, title = {Discovering molecular functions significantly related to phenotypes by combining gene expression data and biological information}, journal = {Bioinformatics}, volume = {21}, number = {13}, year = {2005}, note = {

Al-Shahrour, Fatima Diaz-Uriarte, Ramon Dopazo, Joaquin Evaluation Studies Research Support, Non-U.S. Gov{\textquoteright}t England Bioinformatics (Oxford, England) Bioinformatics. 2005 Jul 1;21(13):2988-93. Epub 2005 Apr 19.

}, pages = {2988-93}, abstract = {

MOTIVATION: The analysis of genome-scale data from different high throughput techniques can be used to obtain lists of genes ordered according to their different behaviours under distinct experimental conditions corresponding to different phenotypes (e.g. differential gene expression between diseased samples and controls, different response to a drug, etc.). The order in which the genes appear in the list is a consequence of the biological roles that the genes play within the cell, which account, at molecular scale, for the macroscopic differences observed between the phenotypes studied. Typically, two steps are followed for understanding the biological processes that differentiate phenotypes at molecular level: first, genes with significant differential expression are selected on the basis of their experimental values and subsequently, the functional properties of these genes are analysed. Instead, we present a simple procedure which combines experimental measurements with available biological information in a way that genes are simultaneously tested in groups related by common functional properties. The method proposed constitutes a very sensitive tool for selecting genes with significant differential behaviour in the experimental conditions tested. RESULTS: We propose the use of a method to scan ordered lists of genes. The method allows the understanding of the biological processes operating at molecular level behind the macroscopic experiment from which the list was generated. This procedure can be useful in situations where it is not possible to obtain statistically significant differences based on the experimental measurements (e.g. low prevalence diseases, etc.). Two examples demonstrate its application in two microarray experiments and the type of information that can be extracted.

}, keywords = {babelomics, Biological Neoplasm Proteins/genetics/*metabolism Phenotype Software Structure-Activity Relationship Systems Integration Tumor Markers, Biological/genetics/*metabolism, Breast Neoplasms/genetics/*metabolism Computer Simulation *Database Management Systems *Databases, Protein Documentation/methods Gene Expression Profiling/*methods Humans *Models}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=15840702}, author = {Fatima Al-Shahrour and Diaz-Uriarte, R. and Dopazo, J.} } @article {15980548, title = {GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data}, journal = {Nucleic Acids Res}, volume = {33}, year = {2005}, note = {

Vaquerizas, Juan M Conde, Lucia Yankilevich, Patricio Cabezon, Amaya Minguez, Pablo Diaz-Uriarte, Ramon Al-Shahrour, Fatima Herrero, Javier Dopazo, Joaquin Research Support, Non-U.S. Gov{\textquoteright}t England Nucleic acids research Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W616-20.

}, pages = {W616-20}, abstract = {

The Gene Expression Profile Analysis Suite, GEPAS, has been running for more than three years. With \>76,000 experiments analysed during the last year and a daily average of almost 300 analyses, GEPAS can be considered a well-established and widely used platform for gene expression microarray data analysis. GEPAS is oriented to the analysis of whole series of experiments. Its design and development have been driven by the demands of the biomedical community, probably the most active collective in the field of microarray users. Although clustering methods have obviously been implemented in GEPAS, our interest has focused more on methods for finding genes differentially expressed among distinct classes of experiments or correlated to diverse clinical outcomes, as well as on building predictors. There is also a great interest in CGH-arrays which fostered the development of the corresponding tool in GEPAS: InSilicoCGH. Much effort has been invested in GEPAS for developing and implementing efficient methods for functional annotation of experiments in the proper statistical framework. Thus, the popular FatiGO has expanded to a suite of programs for functional annotation of experiments, including information on transcription factor binding sites, chromosomal location and tissues. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://www.gepas.org.

}, keywords = {gepas, microarray data analysis}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=15980548}, author = {Vaquerizas, J. M. and L. Conde and Yankilevich, P. and Cabezon, A. and Minguez, P. and Diaz-Uriarte, R. and Fatima Al-Shahrour and Herrero, J. and Dopazo, J.} } @article {15709182, title = {A predictor based on the somatic genomic changes of the BRCA1/BRCA2 breast cancer tumors identifies the non-BRCA1/BRCA2 tumors with BRCA1 promoter hypermethylation}, journal = {Clin Cancer Res}, volume = {11}, number = {3}, year = {2005}, note = {Alvarez, Sara Diaz-Uriarte, Ramon Osorio, Ana Barroso, Alicia Melchor, Lorenzo Paz, Maria Fe Honrado, Emiliano Rodriguez, Raquel Urioste, Miguel Valle, Laura Diez, Orland Cigudosa, Juan Cruz Dopazo, Joaquin Esteller, Manel Benitez, Javier Comparative Study Research Support, Non-U.S. Gov{\textquoteright}t United States Clinical cancer research : an official journal of the American Association for Cancer Research Clin Cancer Res. 2005 Feb 1;11(3):1146-53.}, pages = {1146-53}, abstract = {The genetic changes underlying in the development and progression of familial breast cancer are poorly understood. To identify a somatic genetic signature of tumor progression for each familial group, BRCA1, BRCA2, and non-BRCA1/BRCA2 (BRCAX) tumors, by high-resolution comparative genomic hybridization, we have analyzed 77 tumors previously characterized for BRCA1 and BRCA2 germ line mutations. Based on a combination of the somatic genetic changes observed at the six most different chromosomal regions and the status of the estrogen receptor, we developed using random forests a molecular classifier, which assigns to a given tumor a probability to belong either to the BRCA1 or to the BRCA2 class. Because 76.5\% (26 of 34) of the BRCAX cases were classified with our predictor to the BRCA1 class with a probability of >50\%, we analyzed the BRCA1 promoter region for aberrant methylation in all the BRCAX cases. We found that 15 of the 34 BRCAX analyzed tumors had hypermethylation of the BRCA1 gene. When we considered the predictor, we observed that all the cases with this epigenetic event were assigned to the BRCA1 class with a probability of >50\%. Interestingly, 84.6\% of the cases (11 of 13) assigned to the BRCA1 class with a probability >80\% had an aberrant methylation of the BRCA1 promoter. This fact suggests that somatic BRCA1 inactivation could modify the profile of tumor progression in most of the BRCAX cases.}, keywords = {BRCA1 Protein/*genetics BRCA2 Protein/*genetics Breast Neoplasms/*genetics/pathology Chromosomes, Genetic/*genetics, Human, Human Humans Male Mutation Nucleic Acid Hybridization/methods Promoter Regions, Pair 12/genetics Chromosomes, Pair 15/genetics Chromosomes, Pair 18/genetics Chromosomes, Pair 2/genetics Chromosomes, Pair 8/genetics *DNA Methylation Female Genome}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=15709182}, author = {Alvarez, S. and Diaz-Uriarte, R. and Osorio, A. and Barroso, A. and Melchor, L. and Paz, M. F. and Honrado, E. and Rodriguez, R. and Urioste, M. and Valle, L. and Diez, O. and Cigudosa, J. C. and Dopazo, J. and Esteller, M. and Benitez, J.} } @article {15247094, title = {DNMAD: web-based diagnosis and normalization for microarray data}, journal = {Bioinformatics}, volume = {20}, number = {18}, year = {2004}, note = {Vaquerizas, Juan M Dopazo, Joaquin Diaz-Uriarte, Ramon Research Support, Non-U.S. Gov{\textquoteright}t England Bioinformatics (Oxford, England) Bioinformatics. 2004 Dec 12;20(18):3656-8. Epub 2004 Jul 9.}, pages = {3656-8}, abstract = {SUMMARY: We present a web server for Diagnosis and Normalization of MicroArray Data (DNMAD). DNMAD includes several common data transformations such as spatial and global robust local regression or multiple slide normalization, and allows for detecting several kinds of errors that result from the manipulation and the image analysis of the arrays. This tool offers a user-friendly interface, and is completely integrated within the Gene Expression Pattern Analysis Suite (GEPAS). AVAILABILITY: The tool is accessible on-line at http://dnmad.bioinfo.cnio.es.}, keywords = {Algorithms Database Management Systems Gene Expression Profiling/*methods/standards Information Storage and Retrieval/*methods *Internet Oligonucleotide Array Sequence Analysis/*methods/standards Sequence Alignment/methods Sequence Analysis, DNA/*methods *Software *User-Computer Interface}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=15247094}, author = {Vaquerizas, J. M. and Dopazo, J. and Diaz-Uriarte, R.} } @article {14990455, title = {FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes}, journal = {Bioinformatics}, volume = {20}, number = {4}, year = {2004}, note = {

Al-Shahrour, Fatima Diaz-Uriarte, Ramon Dopazo, Joaquin England Bioinformatics (Oxford, England) Bioinformatics. 2004 Mar 1;20(4):578-80. Epub 2004 Jan 22.

}, pages = {578-80}, abstract = {

We present a simple but powerful procedure to extract Gene Ontology (GO) terms that are significantly over- or under-represented in sets of genes within the context of a genome-scale experiment (DNA microarray, proteomics, etc.). Said procedure has been implemented as a web application, FatiGO, allowing for easy and interactive querying. FatiGO, which takes the multiple-testing nature of statistical contrast into account, currently includes GO associations for diverse organisms (human, mouse, fly, worm and yeast) and the TrEMBL/Swissprot GOAnnotations@EBI correspondences from the European Bioinformatics Institute.

}, keywords = {*Algorithms Artificial Intelligence Databases, babelomics, DNA/*methods *Software, Genetic Gene Expression Profiling/*methods *Hypermedia Information Storage and Retrieval/*methods *Internet *Phylogeny Sequence Alignment/methods Sequence Analysis}, url = {http://bioinformatics.oxfordjournals.org/content/20/4/578.abstract}, author = {Fatima Al-Shahrour and Diaz-Uriarte, R. and Dopazo, J.} } @article {15382261, title = {Gene expression analysis of chromosomal regions with gain or loss of genetic material detected by comparative genomic hybridization}, journal = {Genes Chromosomes Cancer}, volume = {41}, number = {4}, year = {2004}, note = {Melendez, Barbara Diaz-Uriarte, Ramon Cuadros, Marta Martinez-Ramirez, Angel Fernandez-Piqueras, Jose Dopazo, Ana Cigudosa, Juan-Cruz Rivas, Carmen Dopazo, Joaquin Martinez-Delgado, Beatriz Benitez, Javier Research Support, Non-U.S. Gov{\textquoteright}t United States Genes, chromosomes \& cancer Genes Chromosomes Cancer. 2004 Dec;41(4):353-65.}, pages = {353-65}, abstract = {Comparative genomic hybridization (CGH) has been widely used to detect copy number alterations in cancer and to identify regions containing candidate tumor-responsible genes; however, gene expression changes have been described only in highly amplified regions (amplicons). To study the overall impact of slight copy number changes on gene expression, we analyzed 16 T-cell lymphomas by using CGH and a custom-designed cDNA microarray containing 7,657 genes and expressed sequence tags related to tumorigenesis. We evaluated mean gene expression and variability within CGH-altered regions and explored the relationship between the effects of the gene and its position within these regions. Minimally overlapping CGH candidate areas (6q25, 13q21-q22, and 19q13.1) revealed a weak relationship between altered genomic content and gene expression. However, some candidate genes showed modified expression within these regions in the majority of tumors; these candidate genes were evaluated and confirmed in another independent series of 23 T-cell lymphomas by use of the same cDNA microarray and by FISH on a tissue microarray. When all the CGH regions detected for each tumor were considered, we found a significant increase or decrease in the mean expression of the genes contained in gained or lost regions, respectively. In addition, we found that the expression of a gene was dependent not only on its position within an altered region but also on its own mechanism of regulation: genes in the same altered region responded very differently to the gain or loss of genetic material. Supplementary material for this article can be found on the Genes, Chromosomes, and Cancer website at http://www.interscience.wiley.com/jpages/1045-2257/suppmat/index.html.}, keywords = {Chromosomes, Fluorescence Lymphoma, Human, Pair 13/*genetics Chromosomes, Pair 19/*genetics Chromosomes, Pair 6/*genetics Expressed Sequence Tags *Gene Dosage Gene Expression Profiling Humans In Situ Hybridization, T-Cell/*genetics Nucleic Acid Hybridization Oligonucleotide Array Sequence Analysis}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=15382261}, author = {Melendez, B. and Diaz-Uriarte, R. and Cuadros, M. and Martinez-Ramirez, A. and Fernandez-Piqueras, J. and Dopazo, A. and Cigudosa, J. C. and Rivas, C. and Dopazo, J. and Martinez-Delgado, B. and Benitez, J.} } @article {18629097, title = {An approach to inferring transcriptional regulation among genes from large-scale expression data}, journal = {Comp Funct Genomics}, volume = {4}, number = {1}, year = {2003}, note = {Herrero, Javier Diaz-Uriarte, Ramon Dopazo, Joaquin Egypt Comparative and functional genomics Comp Funct Genomics. 2003;4(1):148-54.}, pages = {148-54}, abstract = {The use of DNA microarrays opens up the possibility of measuring the expression levels of thousands of genes simultaneously under different conditions. Time-course experiments allow researchers to study the dynamics of gene interactions. The inference of genetic networks from such measures can give important insights for the understanding of a variety of biological problems. Most of the existing methods for genetic network reconstruction require many experimental data points, or can only be applied to the reconstruction of small subnetworks. Here we present a method that reduces the dimensionality of the dataset and then extracts the significant dynamic correlations among genes. The method requires a number of points achievable in common time-course experiments.}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=18629097}, author = {Herrero, J. and Diaz-Uriarte, R. and Dopazo, J.} } @article {12651726, title = {Gene expression data preprocessing}, journal = {Bioinformatics}, volume = {19}, number = {5}, year = {2003}, note = {Herrero, J Diaz-Uriarte, R Dopazo, J England Bioinformatics (Oxford, England) Bioinformatics. 2003 Mar 22;19(5):655-6.}, pages = {655-6}, abstract = {We present an interactive web tool for preprocessing microarray gene expression data. It analyses the data, suggests the most appropriate transformations and proceeds with them after user agreement. The normal preprocessing steps include scale transformations, management of missing values, replicate handling, flat pattern filtering and pattern standardization and they are required before performing any pattern analysis. The processed data set can be sent to other pattern analysis tools.}, keywords = {*Database Management Systems Gene Expression Profiling/*methods Information Storage and Retrieval/methods Internet Oligonucleotide Array Sequence Analysis/*methods Sequence Alignment/*methods Sequence Analysis, DNA/*methods *Software *User-Computer Interface}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=12651726}, author = {Herrero, J. and Diaz-Uriarte, R. and Dopazo, J.} } @article {12824345, title = {GEPAS: A web-based resource for microarray gene expression data analysis}, journal = {Nucleic Acids Res}, volume = {31}, number = {13}, year = {2003}, note = {

Herrero, Javier Al-Shahrour, Fatima Diaz-Uriarte, Ramon Mateos, Alvaro Vaquerizas, Juan M Santoyo, Javier Dopazo, Joaquin Research Support, Non-U.S. Gov{\textquoteright}t England Nucleic acids research Nucleic Acids Res. 2003 Jul 1;31(13):3461-7.

}, pages = {3461-7}, abstract = {

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 (http://bioinfo.cnio.es).

}, keywords = {gepas, microarray data analysis}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=12824345}, author = {Herrero, J. and Fatima Al-Shahrour and Diaz-Uriarte, R. and A. Mateos and Vaquerizas, J. M. and J. Santoyo and Dopazo, J.} }