@article {16845056, title = {Next station in microarray data analysis: GEPAS}, journal = {Nucleic Acids Res}, volume = {34}, year = {2006}, note = {

Montaner, David Tarraga, Joaquin Huerta-Cepas, Jaime Burguet, Jordi Vaquerizas, Juan M Conde, Lucia Minguez, Pablo Vera, Javier Mukherjee, Sach Valls, Joan Pujana, Miguel A G Alloza, Eva Herrero, Javier Al-Shahrour, Fatima Dopazo, Joaquin Research Support, Non-U.S. Gov{\textquoteright}t England Nucleic acids research Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W486-91.

}, pages = {W486-91}, abstract = {

The Gene Expression Profile Analysis Suite (GEPAS) has been running for more than four years. During this time it has evolved to keep pace with the new interests and trends in the still changing world of microarray data analysis. GEPAS has been designed to provide an intuitive although powerful web-based interface that offers diverse analysis options from the early step of preprocessing (normalization of Affymetrix and two-colour microarray experiments and other preprocessing options), to the final step of the functional annotation of the experiment (using Gene Ontology, pathways, PubMed abstracts etc.), and include different possibilities for clustering, gene selection, class prediction and array-comparative genomic hybridization management. GEPAS is extensively used by researchers of many countries and its records indicate an average usage rate of 400 experiments per day. 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=16845056}, author = {Montaner, D. and Tarraga, J. and Huerta-Cepas, J. and Burguet, J. and Vaquerizas, J. M. and L. Conde and Minguez, P. and Vera, J. and Mukherjee, S. and Valls, J. and Pujana, M. A. and Alloza, E. and Herrero, J. and Fatima Al-Shahrour 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 {15215434, title = {New challenges in gene expression data analysis and the extended GEPAS}, journal = {Nucleic Acids Res}, volume = {32}, year = {2004}, note = {

Herrero, Javier Vaquerizas, Juan M Al-Shahrour, Fatima Conde, Lucia Mateos, Alvaro Diaz-Uriarte, Javier Santoyo Ramon Dopazo, Joaquin England Nucleic acids research Nucleic Acids Res. 2004 Jul 1;32(Web Server issue):W485-91.

}, pages = {W485-91}, abstract = {

Since the first papers published in the late nineties, including, for the first time, a comprehensive analysis of microarray data, the number of questions that have been addressed through this technique have both increased and diversified. Initially, interest focussed on genes coexpressing across sets of experimental conditions, implying, essentially, the use of clustering techniques. Recently, however, interest has focussed more on finding genes differentially expressed among distinct classes of experiments, or correlated to diverse clinical outcomes, as well as in building predictors. In addition to this, the availability of accurate genomic data and the recent implementation of CGH arrays has made mapping expression and genomic data on the chromosomes possible. There is also a clear demand for methods that allow the automatic transfer of biological information to the results of microarray experiments. Different initiatives, such as the Gene Ontology (GO) consortium, pathways databases, protein functional motifs, etc., provide curated annotations for genes. Whereas many resources on the web focus mainly on clustering methods, GEPAS has evolved to cope with the aforementioned new challenges that have recently arisen in the field of microarray data analysis. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://gepas.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=15215434}, author = {Herrero, J. and Vaquerizas, J. M. and Fatima Al-Shahrour and L. Conde and A. Mateos and Diaz-Uriarte, J. S. and Dopazo, 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 {12695324, title = {Comparing bacterial genomes through conservation profiles}, journal = {Genome Res}, volume = {13}, number = {5}, year = {2003}, note = {Martin, Maria J Herrero, Javier Mateos, Alvaro Dopazo, Joaquin Comparative Study United States Genome research Genome Res. 2003 May;13(5):991-8. Epub 2003 Apr 14.}, pages = {991-8}, abstract = {We constructed two-dimensional representations of profiles of gene conservation across different genomes using the genome of Escherichia coli as a model. These profiles permit both the visualization at the genome level of different traits in the organism studied and, at the same time, reveal features related to the genomes analyzed (such as defective genomes or genomes that lack a particular system). Conserved genes are not uniformly distributed along the E. coli genome but tend to cluster together. The study of gene distribution patterns across genomes is important for the understanding of how sets of genes seem to be dependent on each other, probably having some functional link. This provides additional evidence that can be used for the elucidation of the function of unannotated genes. Clustering these patterns produces families of genes which can be arranged in a hierarchy of closeness. In this way, functions can be defined at different levels of generality depending on the level of the hierarchy that is studied. The combined study of conservation and phenotypic traits opens up the possibility of defining phenotype/genotype associations, and ultimately inferring the gene or genes responsible for a particular trait.}, keywords = {Bacterial Genotype Models, Bacterial/genetics Cluster Analysis Conserved Sequence/*genetics DNA, Bacterial/genetics Escherichia coli/classification/*genetics Evolution, Bacterial/genetics Gene Order/genetics Genes, Bacterial/genetics/physiology *Genome, Chromosome Mapping/methods Chromosomes, Genetic Phenotype Phylogeny Sequence Homology, Molecular Gene Expression Profiling/methods Gene Expression Regulation, Nucleic Acid Species Specificity Terminology as Topic}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=12695324}, author = {Martin, M. J. and Herrero, J. and A. Mateos 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.} } @article {12141992, title = {Bioinformatics methods for the analysis of expression arrays: data clustering and information extraction}, journal = {J Biotechnol}, volume = {98}, number = {2-3}, year = {2002}, note = {Tamames, Javier Clark, Dominic Herrero, Javier Dopazo, Joaquin Blaschke, Christian Fernandez, Jose M Oliveros, Juan C Valencia, Alfonso Review Netherlands Journal of biotechnology J Biotechnol. 2002 Sep 25;98(2-3):269-83.}, pages = {269-83}, abstract = {Expression arrays facilitate the monitoring of changes in the expression patterns of large collections of genes. The analysis of expression array data has become a computationally-intensive task that requires the development of bioinformatics technology for a number of key stages in the process, such as image analysis, database storage, gene clustering and information extraction. Here, we review the current trends in each of these areas, with particular emphasis on the development of the related technology being carried out within our groups.}, keywords = {Abstracting and Indexing as Topic/methods *Cluster Analysis *Database Management Systems Databases, Computer-Assisted/methods Information Storage and Retrieval/*methods Internet Medline National Library of Medicine (U.S.) Oligonucleotide Array Sequence Analysis/*methods United States, Genetic Gene Expression Gene Expression Profiling/*methods Image Processing}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=12141992}, author = {J. Tamames and Clark, D. and Herrero, J. and Dopazo, J. and Blaschke, C. and Fernandez, J. M. and Oliveros, J. C. and Valencia, A.} } @article {12645919, title = {Combining hierarchical clustering and self-organizing maps for exploratory analysis of gene expression patterns}, journal = {J Proteome Res}, volume = {1}, number = {5}, year = {2002}, note = {Herrero, Javier Dopazo, Joaquin Research Support, Non-U.S. Gov{\textquoteright}t United States Journal of proteome research J Proteome Res. 2002 Sep-Oct;1(5):467-70.}, pages = {467-70}, abstract = {Self-organizing maps (SOM) constitute an alternative to classical clustering methods because of its linear run times and superior performance to deal with noisy data. Nevertheless, the clustering obtained with SOM is dependent on the relative sizes of the clusters. Here, we show how the combination of SOM with hierarchical clustering methods constitutes an excellent tool for exploratory analysis of massive data like DNA microarray expression patterns.}, keywords = {Cluster Analysis Computational Biology/methods *Gene Expression Genes, Fungal/genetics *Genome Oligonucleotide Array Sequence Analysis/*methods Statistics as Topic/*methods Time Factors}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=12645919}, author = {Herrero, J. and Dopazo, J.} } @article {12414529, title = {Identification of genes involved in resistance to interferon-alpha in cutaneous T-cell lymphoma}, journal = {Am J Pathol}, volume = {161}, number = {5}, year = {2002}, note = {Tracey, Lorraine Villuendas, Raquel Ortiz, Pablo Dopazo, Ana Spiteri, Inmaculada Lombardia, Luis Rodriguez-Peralto, Jose L Fernandez-Herrera, Jesus Hernandez, Almudena Fraga, Javier Dominguez, Orlando Herrero, Javier Alonso, Miguel A Dopazo, Joaquin Piris, Miguel A Research Support, Non-U.S. Gov{\textquoteright}t United States The American journal of pathology Am J Pathol. 2002 Nov;161(5):1825-37.}, pages = {1825-37}, abstract = {Interferon-alpha therapy has been shown to be active in the treatment of mycosis fungoides although the individual response to this therapy is unpredictable and dependent on essentially unknown factors. In an effort to better understand the molecular mechanisms of interferon-alpha resistance we have developed an interferon-alpha resistant variant from a sensitive cutaneous T-cell lymphoma cell line. We have performed expression analysis to detect genes differentially expressed between both variants using a cDNA microarray including 6386 cancer-implicated genes. The experiments showed that resistance to interferon-alpha is consistently associated with changes in the expression of a set of 39 genes, involved in signal transduction, apoptosis, transcription regulation, and cell growth. Additional studies performed confirm that STAT1 and STAT3 expression and interferon-alpha induction and activation are not altered between both variants. The gene MAL, highly overexpressed by resistant cells, was also found to be expressed by tumoral cells in a series of cutaneous T-cell lymphoma patients treated with interferon-alpha and/or photochemotherapy. MAL expression was associated with longer time to complete remission. Time-course experiments of the sensitive and resistant cells showed a differential expression of a subset of genes involved in interferon-response (1 to 4 hours), cell growth and apoptosis (24 to 48 hours.), and signal transduction.}, keywords = {Antineoplastic Agents/*pharmacology/therapeutic use Carrier Proteins/biosynthesis/genetics DNA-Binding Proteins/biosynthesis/genetics Drug Resistance, Biological Oligonucleotide Array Sequence Analysis RNA, Cultured, Cutaneous/diagnosis/drug therapy/*genetics/metabolism *Membrane Glycoproteins Models, Interleukin-1 Reproducibility of Results STAT1 Transcription Factor STAT3 Transcription Factor Trans-Activators/biosynthesis/genetics Tumor Cells, Neoplasm Gene Expression Profiling *Gene Expression Regulation, Neoplasm/biosynthesis *Receptors, Neoplastic Humans Interferon-alpha/*pharmacology/therapeutic use Kinetics Lymphoma, T-Cell}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=12414529}, author = {Tracey, L. and Villuendas, R. and Ortiz, P. and Dopazo, A. and Spiteri, I. and Lombardia, L. and Rodriguez-Peralto, J. L. and Fernandez-Herrera, J. and Hernandez, A. and Fraga, J. and Dominguez, O. and Herrero, J. and Alonso, M. A. and Dopazo, J. and Piris, M. A.} } @article {11442348, title = {Annotated draft genomic sequence from a Streptococcus pneumoniae type 19F clinical isolate}, journal = {Microb Drug Resist}, volume = {7}, number = {2}, year = {2001}, note = {Dopazo, J Mendoza, A Herrero, J Caldara, F Humbert, Y Friedli, L Guerrier, M Grand-Schenk, E Gandin, C de Francesco, M Polissi, A Buell, G Feger, G Garcia, E Peitsch, M Garcia-Bustos, J F United States Microbial drug resistance (Larchmont, N.Y.) Microb Drug Resist. 2001 Summer;7(2):99-125.}, pages = {99-125}, abstract = {The public availability of numerous microbial genomes is enabling the analysis of bacterial biology in great detail and with an unprecedented, organism-wide and taxon-wide, broad scope. Streptococcus pneumoniae is one of the most important bacterial pathogens throughout the world. We present here sequences and functional annotations for 2.1-Mbp of pneumococcal DNA, covering more than 90\% of the total estimated size of the genome. The sequenced strain is a clinical isolate resistant to macrolides and tetracycline. It carries a type 19F capsular locus, but multilocus sequence typing for several conserved genetic loci suggests that the strain sequenced belongs to a pneumococcal lineage that most often expresses a serotype 15 capsular polysaccharide. A total of 2,046 putative open reading frames (ORFs) longer than 100 amino acids were identified (average of 1,009 bp per ORF), including all described two-component systems and aminoacyl tRNA synthetases. Comparisons to other complete, or nearly complete, bacterial genomes were made and are presented in a graphical form for all the predicted proteins.}, keywords = {Bacterial Molecular Sequence Data Pneumococcal Infections/*microbiology Prokaryotic Cells RNA, Bacterial/chemistry/genetics Genes, Bacterial/genetics *Genome, DNA, Transfer/metabolism Streptococcus pneumoniae/*genetics}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=11442348}, author = {Dopazo, J. and Mendoza, A. and Herrero, J. and Caldara, F. and Humbert, Y. and Friedli, L. and Guerrier, M. and Grand-Schenk, E. and Gandin, C. and de Francesco, M. and Polissi, A. and Buell, G. and Feger, G. and Garcia, E. and Peitsch, M. and Garcia-Bustos, J. F.} } @article {11238068, title = {A hierarchical unsupervised growing neural network for clustering gene expression patterns}, journal = {Bioinformatics}, volume = {17}, number = {2}, year = {2001}, note = {Herrero, J Valencia, A Dopazo, J Research Support, Non-U.S. Gov{\textquoteright}t England Bioinformatics (Oxford, England) Bioinformatics. 2001 Feb;17(2):126-36.}, pages = {126-36}, abstract = {MOTIVATION: We describe a new approach to the analysis of gene expression data coming from DNA array experiments, using an unsupervised neural network. DNA array technologies allow monitoring thousands of genes rapidly and efficiently. One of the interests of these studies is the search for correlated gene expression patterns, and this is usually achieved by clustering them. The Self-Organising Tree Algorithm, (SOTA) (Dopazo,J. and Carazo,J.M. (1997) J. Mol. Evol., 44, 226-233), is a neural network that grows adopting the topology of a binary tree. The result of the algorithm is a hierarchical cluster obtained with the accuracy and robustness of a neural network. RESULTS: SOTA clustering confers several advantages over classical hierarchical clustering methods. SOTA is a divisive method: the clustering process is performed from top to bottom, i.e. the highest hierarchical levels are resolved before going to the details of the lowest levels. The growing can be stopped at the desired hierarchical level. Moreover, a criterion to stop the growing of the tree, based on the approximate distribution of probability obtained by randomisation of the original data set, is provided. By means of this criterion, a statistical support for the definition of clusters is proposed. In addition, obtaining average gene expression patterns is a built-in feature of the algorithm. Different neurons defining the different hierarchical levels represent the averages of the gene expression patterns contained in the clusters. Since SOTA runtimes are approximately linear with the number of items to be classified, it is especially suitable for dealing with huge amounts of data. The method proposed is very general and applies to any data providing that they can be coded as a series of numbers and that a computable measure of similarity between data items can be used. AVAILABILITY: A server running the program can be found at: http://bioinfo.cnio.es/sotarray.}, keywords = {*Algorithms Automatic Data Processing *Gene Expression Profiling *Neural Networks (Computer) *Oligonucleotide Array Sequence Analysis}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=11238068}, author = {Herrero, J. and Valencia, A. and Dopazo, J.} }