@article {439, title = {Integrated gene set analysis for microRNA studies.}, journal = {Bioinformatics}, volume = {32}, year = {2016}, month = {2016 09 15}, pages = {2809-16}, abstract = {

MOTIVATION: Functional interpretation of miRNA expression data is currently done in a three step procedure: select differentially expressed miRNAs, find their target genes, and carry out gene set overrepresentation analysis Nevertheless, major limitations of this approach have already been described at the gene level, while some newer arise in the miRNA scenario.Here, we propose an enhanced methodology that builds on the well-established gene set analysis paradigm. Evidence for differential expression at the miRNA level is transferred to a gene differential inhibition score which is easily interpretable in terms of gene sets or pathways. Such transferred indexes account for the additive effect of several miRNAs targeting the same gene, and also incorporate cancellation effects between cases and controls. Together, these two desirable characteristics allow for more accurate modeling of regulatory processes.

RESULTS: We analyze high-throughput sequencing data from 20 different cancer types and provide exhaustive reports of gene and Gene Ontology-term deregulation by miRNA action.

AVAILABILITY AND IMPLEMENTATION: The proposed methodology was implemented in the Bioconductor library mdgsa http://bioconductor.org/packages/mdgsa For the purpose of reproducibility all of the scripts are available at https://github.com/dmontaner-papers/gsa4mirna

CONTACT: : david.montaner@gmail.com

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, keywords = {Computational Biology, Gene Expression Profiling, Gene ontology, Gene Regulatory Networks, High-Throughput Nucleotide Sequencing, Humans, MicroRNAs, Neoplasms, Reproducibility of Results}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btw334}, author = {Garcia-Garcia, Francisco and Panadero, Joaquin and Dopazo, Joaquin and Montaner, David} } @article {491, title = {Pathway network inference from gene expression data.}, journal = {BMC Syst Biol}, volume = {8 Suppl 2}, year = {2014}, month = {2014}, pages = {S7}, abstract = {

BACKGROUND: The development of high-throughput omics technologies enabled genome-wide measurements of the activity of cellular elements and provides the analytical resources for the progress of the Systems Biology discipline. Analysis and interpretation of gene expression data has evolved from the gene to the pathway and interaction level, i.e. from the detection of differentially expressed genes, to the establishment of gene interaction networks and the identification of enriched functional categories. Still, the understanding of biological systems requires a further level of analysis that addresses the characterization of the interaction between functional modules.

RESULTS: We present a novel computational methodology to study the functional interconnections among the molecular elements of a biological system. The PANA approach uses high-throughput genomics measurements and a functional annotation scheme to extract an activity profile from each functional block -or pathway- followed by machine-learning methods to infer the relationships between these functional profiles. The result is a global, interconnected network of pathways that represents the functional cross-talk within the molecular system. We have applied this approach to describe the functional transcriptional connections during the yeast cell cycle and to identify pathways that change their connectivity in a disease condition using an Alzheimer example.

CONCLUSIONS: PANA is a useful tool to deepen in our understanding of the functional interdependences that operate within complex biological systems. We show the approach is algorithmically consistent and the inferred network is well supported by the available functional data. The method allows the dissection of the molecular basis of the functional connections and we describe the different regulatory mechanisms that explain the network{\textquoteright}s topology obtained for the yeast cell cycle data.

}, keywords = {Alzheimer Disease, Cell Cycle, DNA Replication, Gene Expression Profiling, Gene Regulatory Networks, Gluconeogenesis, Glycolysis, Oxidative Phosphorylation, Proteolysis, Purines, Saccharomyces cerevisiae, Systems biology, Ubiquitin}, issn = {1752-0509}, doi = {10.1186/1752-0509-8-S2-S7}, author = {Ponzoni, Ignacio and Nueda, Mar{\'\i}a and Tarazona, Sonia and G{\"o}tz, Stefan and Montaner, David and Dussaut, Julieta and Dopazo, Joaquin and Conesa, Ana} } @article {503, title = {Defining the genomic signature of totipotency and pluripotency during early human development.}, journal = {PLoS One}, volume = {8}, year = {2013}, month = {2013}, pages = {e62135}, abstract = {

The genetic mechanisms governing human pre-implantation embryo development and the in vitro counterparts, human embryonic stem cells (hESCs), still remain incomplete. Previous global genome studies demonstrated that totipotent blastomeres from day-3 human embryos and pluripotent inner cell masses (ICMs) from blastocysts, display unique and differing transcriptomes. Nevertheless, comparative gene expression analysis has revealed that no significant differences exist between hESCs derived from blastomeres versus those obtained from ICMs, suggesting that pluripotent hESCs involve a new developmental progression. To understand early human stages evolution, we developed an undifferentiation network signature (UNS) and applied it to a differential gene expression profile between single blastomeres from day-3 embryos, ICMs and hESCs. This allowed us to establish a unique signature composed of highly interconnected genes characteristic of totipotency (61 genes), in vivo pluripotency (20 genes), and in vitro pluripotency (107 genes), and which are also proprietary according to functional analysis. This systems biology approach has led to an improved understanding of the molecular and signaling processes governing human pre-implantation embryo development, as well as enabling us to comprehend how hESCs might adapt to in vitro culture conditions.

}, keywords = {Blastocyst Inner Cell Mass, Blastomeres, Cell Differentiation, Embryonic Development, Embryonic Stem Cells, Gene Expression Profiling, Gene Regulatory Networks, Genome, Human, Humans, Molecular Sequence Annotation, Pluripotent Stem Cells, Totipotent Stem Cells}, issn = {1932-6203}, doi = {10.1371/journal.pone.0062135}, author = {Galan, Amparo and Diaz-Gimeno, Patricia and Poo, Maria Eugenia and Valbuena, Diana and Sanchez, Eva and Ruiz, Veronica and Dopazo, Joaquin and Montaner, David and Conesa, Ana and Simon, Carlos} } @article {535, title = {Large-scale transcriptional profiling and functional assays reveal important roles for Rho-GTPase signalling and SCL during haematopoietic differentiation of human embryonic stem cells.}, journal = {Hum Mol Genet}, volume = {20}, year = {2011}, month = {2011 Dec 15}, pages = {4932-46}, abstract = {

Understanding the transcriptional cues that direct differentiation of human embryonic stem cells (hESCs) and human-induced pluripotent stem cells to defined and functional cell types is essential for future clinical applications. In this study, we have compared transcriptional profiles of haematopoietic progenitors derived from hESCs at various developmental stages of a feeder- and serum-free differentiation method and show that the largest transcriptional changes occur during the first 4 days of differentiation. Data mining on the basis of molecular function revealed Rho-GTPase signalling as a key regulator of differentiation. Inhibition of this pathway resulted in a significant reduction in the numbers of emerging haematopoietic progenitors throughout the differentiation window, thereby uncovering a previously unappreciated role for Rho-GTPase signalling during human haematopoietic development. Our analysis indicated that SCL was the 11th most upregulated transcript during the first 4 days of the hESC differentiation process. Overexpression of SCL in hESCs promoted differentiation to meso-endodermal lineages, the emergence of haematopoietic and erythro-megakaryocytic progenitors and accelerated erythroid differentiation. Importantly, intrasplenic transplantation of SCL-overexpressing hESC-derived haematopoietic cells enhanced recovery from induced acute anaemia without significant cell engraftment, suggesting a paracrine-mediated effect.

}, keywords = {Acute Disease, Anemia, Hemolytic, Animals, Basic Helix-Loop-Helix Transcription Factors, Cell Differentiation, Cell Line, Cell Lineage, Cluster Analysis, Embryonic Stem Cells, Erythroid Cells, Flow Cytometry, Gene Expression Profiling, Hematopoietic Stem Cells, Humans, Mice, Myeloid Cells, Paracrine Communication, Proto-Oncogene Proteins, Reverse Transcriptase Polymerase Chain Reaction, rho GTP-Binding Proteins, Signal Transduction, Stem Cell Transplantation, T-Cell Acute Lymphocytic Leukemia Protein 1, Transcriptome}, issn = {1460-2083}, doi = {10.1093/hmg/ddr431}, author = {Yung, Sun and Ledran, Maria and Moreno-Gimeno, Inmaculada and Conesa, Ana and Montaner, David and Dopazo, Joaquin and Dimmick, Ian and Slater, Nicholas J and Marenah, Lamin and Real, Pedro J and Paraskevopoulou, Iliana and Bisbal, Viviana and Burks, Deborah and Santibanez-Koref, Mauro and Moreno, Ruben and Mountford, Joanne and Menendez, Pablo and Armstrong, Lyle and Lako, Majlinda} } @article {547, title = {DNA methylation epigenotypes in breast cancer molecular subtypes.}, journal = {Breast Cancer Res}, volume = {12}, year = {2010}, month = {2010}, pages = {R77}, abstract = {

INTRODUCTION: Identification of gene expression based breast cancer subtypes is considered as a critical means of prognostication. Genetic mutations along with epigenetic alterations contribute to gene expression changes occurring in breast cancer. So far, these epigenetic contributions to sporadic breast cancer subtypes have not been well characterized, and there is only a limited understanding of the epigenetic mechanisms affected in those particular breast cancer subtypes. The present study was undertaken to dissect the breast cancer methylome and deliver specific epigenotypes associated with particular breast cancer subtypes.

METHODS: Using a microarray approach we analyzed DNA methylation in regulatory regions of 806 cancer related genes in 28 breast cancer paired samples. We subsequently performed substantial technical and biological validation by Pyrosequencing, investigating the top qualifying 19 CpG regions in independent cohorts encompassing 47 basal-like, 44 ERBB2+ overexpressing, 48 luminal A and 48 luminal B paired breast cancer/adjacent tissues. Using all-subset selection method, we identified the most subtype predictive methylation profiles in multivariable logistic regression analysis.

RESULTS: The approach efficiently recognized 15 individual CpG loci differentially methylated in breast cancer tumor subtypes. We further identify novel subtype specific epigenotypes which clearly demonstrate the differences in the methylation profiles of basal-like and human epidermal growth factor 2 (HER2)-overexpressing tumors.

CONCLUSIONS: Our results provide evidence that well defined DNA methylation profiles enables breast cancer subtype prediction and support the utilization of this biomarker for prognostication and therapeutic stratification of patients with breast cancer.

}, keywords = {Aged, Breast Neoplasms, CpG Islands, DNA Methylation, Epigenesis, Genetic, Female, Gene Expression Profiling, Genes, p53, Genotype, Humans, Ki-67 Antigen, Middle Aged, mutation, Neoplasm Grading, Oligonucleotide Array Sequence Analysis, Receptor, ErbB-2, Tumor Suppressor Protein p53}, issn = {1465-542X}, doi = {10.1186/bcr2721}, author = {Bediaga, Naiara G and Acha-Sagredo, Amelia and Guerra, Isabel and Viguri, Amparo and Albaina, Carmen and Ruiz Diaz, Irune and Rezola, Ricardo and Alberdi, Maria Jesus and Dopazo, Joaquin and Montaner, David and Renobales, Mertxe and Fernandez, Agustin F and Field, John K and Fraga, Mario F and Liloglou, Triantafillos and de Pancorbo, Marian M} } @article {553, title = {Functional genomics of 5- to 8-cell stage human embryos by blastomere single-cell cDNA analysis.}, journal = {PLoS One}, volume = {5}, year = {2010}, month = {2010 Oct 26}, pages = {e13615}, abstract = {

Blastomere fate and embryonic genome activation (EGA) during human embryonic development are unsolved areas of high scientific and clinical interest. Forty-nine blastomeres from 5- to 8-cell human embryos have been investigated following an efficient single-cell cDNA amplification protocol to provide a template for high-density microarray analysis. The previously described markers, characteristic of Inner Cell Mass (ICM) (n = 120), stemness (n = 190) and Trophectoderm (TE) (n = 45), were analyzed, and a housekeeping pattern of 46 genes was established. All the human blastomeres from the 5- to 8-cell stage embryo displayed a common gene expression pattern corresponding to ICM markers (e.g., DDX3, FOXD3, LEFTY1, MYC, NANOG, POU5F1), stemness (e.g., POU5F1, DNMT3B, GABRB3, SOX2, ZFP42, TERT), and TE markers (e.g., GATA6, EOMES, CDX2, LHCGR). The EGA profile was also investigated between the 5-6- and 8-cell stage embryos, and compared to the blastocyst stage. Known genes (n = 92) such as depleted maternal transcripts (e.g., CCNA1, CCNB1, DPPA2) and embryo-specific activation (e.g., POU5F1, CDH1, DPPA4), as well as novel genes, were confirmed. In summary, the global single-cell cDNA amplification microarray analysis of the 5- to 8-cell stage human embryos reveals that blastomere fate is not committed to ICM or TE. Finally, new EGA features in human embryogenesis are presented.

}, keywords = {Blastomeres, DNA, Complementary, Gene Expression Profiling, Genomics, Humans, Oligonucleotide Array Sequence Analysis}, issn = {1932-6203}, doi = {10.1371/journal.pone.0013615}, author = {Galan, Amparo and Montaner, David and P{\'o}o, M Eugenia and Valbuena, Diana and Ruiz, Veronica and Aguilar, Crist{\'o}bal and Dopazo, Joaquin and Simon, Carlos} } @article {572, title = {Hypoxia promotes efficient differentiation of human embryonic stem cells to functional endothelium.}, journal = {Stem Cells}, volume = {28}, year = {2010}, month = {2010 Mar 31}, pages = {407-18}, abstract = {

Early development of mammalian embryos occurs in an environment of relative hypoxia. Nevertheless, human embryonic stem cells (hESC), which are derived from the inner cell mass of blastocyst, are routinely cultured under the same atmospheric conditions (21\% O(2)) as somatic cells. We hypothesized that O(2) levels modulate gene expression and differentiation potential of hESC, and thus, we performed gene profiling of hESC maintained under normoxic or hypoxic (1\% or 5\% O(2)) conditions. Our analysis revealed that hypoxia downregulates expression of pluripotency markers in hESC but increases significantly the expression of genes associated with angio- and vasculogenesis including vascular endothelial growth factor and angiopoitein-like proteins. Consequently, we were able to efficiently differentiate hESC to functional endothelial cells (EC) by varying O(2) levels; after 24 hours at 5\% O(2), more than 50\% of cells were CD34+. Transplantation of resulting endothelial-like cells improved both systolic function and fractional shortening in a rodent model of myocardial infarction. Moreover, analysis of the infarcted zone revealed that transplanted EC reduced the area of fibrous scar tissue by 50\%. Thus, use of hypoxic conditions to specify the endothelial lineage suggests a novel strategy for cellular therapies aimed at repair of damaged vasculature in pathologies such as cerebral ischemia and myocardial infarction.

}, keywords = {Angiopoietin-1, Animals, biomarkers, Cell Culture Techniques, Cell Differentiation, Cell Hypoxia, Cell Transplantation, Cells, Cultured, Down-Regulation, Embryonic Stem Cells, Endothelial Cells, Gene Expression Profiling, Gene Expression Regulation, Humans, Male, Myocardial Infarction, Neovascularization, Physiologic, Oxygen, Pluripotent Stem Cells, Rats, Rats, Nude, Vascular Endothelial Growth Factor A}, issn = {1549-4918}, doi = {10.1002/stem.295}, author = {Prado-Lopez, Sonia and Conesa, Ana and Armi{\~n}{\'a}n, Ana and Mart{\'\i}nez-Losa, Magdalena and Escobedo-Lucea, Carmen and Gandia, Carolina and Tarazona, Sonia and Melguizo, Dario and Blesa, David and Montaner, David and Sanz-Gonz{\'a}lez, Silvia and Sep{\'u}lveda, Pilar and G{\"o}tz, Stefan and O{\textquoteright}Connor, Jos{\'e} Enrique and Moreno, Ruben and Dopazo, Joaquin and Burks, Deborah J and Stojkovic, Miodrag} } @article {574, title = {Multidimensional gene set analysis of genomic data.}, journal = {PLoS One}, volume = {5}, year = {2010}, month = {2010 Apr 27}, pages = {e10348}, abstract = {

Understanding the functional implications of changes in gene expression, mutations, etc., is the aim of most genomic experiments. To achieve this, several functional profiling methods have been proposed. Such methods study the behaviour of different gene modules (e.g. gene ontology terms) in response to one particular variable (e.g. differential gene expression). In spite to the wealth of information provided by functional profiling methods, a common limitation to all of them is their inherent unidimensional nature. In order to overcome this restriction we present a multidimensional logistic model that allows studying the relationship of gene modules with different genome-scale measurements (e.g. differential expression, genotyping association, methylation, copy number alterations, heterozygosity, etc.) simultaneously. Moreover, the relationship of such functional modules with the interactions among the variables can also be studied, which produces novel results impossible to be derived from the conventional unidimensional functional profiling methods. We report sound results of gene sets associations that remained undetected by the conventional one-dimensional gene set analysis in several examples. Our findings demonstrate the potential of the proposed approach for the discovery of new cell functionalities with complex dependences on more than one variable.

}, keywords = {Databases, Genetic, Gene Expression Profiling, Gene Regulatory Networks, Genome, Human, Genomics, Humans, Models, Statistical}, issn = {1932-6203}, doi = {10.1371/journal.pone.0010348}, author = {Montaner, David and Dopazo, Joaquin} } @article {582, title = {Gene set internal coherence in the context of functional profiling.}, journal = {BMC Genomics}, volume = {10}, year = {2009}, month = {2009 Apr 27}, pages = {197}, abstract = {

BACKGROUND: Functional profiling methods have been extensively used in the context of high-throughput experiments and, in particular, in microarray data analysis. Such methods use available biological information to define different types of functional gene modules (e.g. gene ontology -GO-, KEGG pathways, etc.) whose representation in a pre-defined list of genes is further studied. In the most popular type of microarray experimental designs (e.g. up- or down-regulated genes, clusters of co-expressing genes, etc.) or in other genomic experiments (e.g. Chip-on-chip, epigenomics, etc.) these lists are composed by genes with a high degree of co-expression. Therefore, an implicit assumption in the application of functional profiling methods within this context is that the genes corresponding to the modules tested are effectively defining sets of co-expressing genes. Nevertheless not all the functional modules are biologically coherent entities in terms of co-expression, which will eventually hinder its detection with conventional methods of functional enrichment.

RESULTS: Using a large collection of microarray data we have carried out a detailed survey of internal correlation in GO terms and KEGG pathways, providing a coherence index to be used for measuring functional module co-regulation. An unexpected low level of internal correlation was found among the modules studied. Only around 30\% of the modules defined by GO terms and 57\% of the modules defined by KEGG pathways display an internal correlation higher than the expected by chance.This information on the internal correlation of the genes within the functional modules can be used in the context of a logistic regression model in a simple way to improve their detection in gene expression experiments.

CONCLUSION: For the first time, an exhaustive study on the internal co-expression of the most popular functional categories has been carried out. Interestingly, the real level of coexpression within many of them is lower than expected (or even inexistent), which will preclude its detection by means of most conventional functional profiling methods. If the gene-to-function correlation information is used in functional profiling methods, the results obtained improve the ones obtained by conventional enrichment methods.

}, keywords = {Algorithms, Breast Neoplasms, Carcinoma, Intraductal, Noninfiltrating, Computational Biology, Databases, Nucleic Acid, Female, Gene Expression Profiling, Genomics, Humans, Oligonucleotide Array Sequence Analysis, Papillomavirus Infections, Reproducibility of Results}, issn = {1471-2164}, doi = {10.1186/1471-2164-10-197}, author = {Montaner, David and Minguez, Pablo and Al-Shahrour, F{\'a}tima and Dopazo, Joaquin} } @article {590, title = {Direct functional assessment of the composite phenotype through multivariate projection strategies.}, journal = {Genomics}, volume = {92}, year = {2008}, month = {2008 Dec}, pages = {373-83}, abstract = {

We present a novel approach for the analysis of transcriptomics data that integrates functional annotation of gene sets with expression values in a multivariate fashion, and directly assesses the relation of functional features to a multivariate space of response phenotypical variables. Multivariate projection methods are used to obtain new correlated variables for a set of genes that share a given function. These new functional variables are then related to the response variables of interest. The analysis of the principal directions of the multivariate regression allows for the identification of gene function features correlated with the phenotype. Two different transcriptomics studies are used to illustrate the statistical and interpretative aspects of the methodology. We demonstrate the superiority of the proposed method over equivalent approaches.

}, keywords = {Breast Neoplasms, Computational Biology, Databases, Genetic, Female, Gene Expression Profiling, Humans, Mathematical Computing, Multivariate Analysis, Phenotype}, issn = {1089-8646}, doi = {10.1016/j.ygeno.2008.05.015}, author = {Conesa, Ana and Bro, Rasmus and Garcia-Garcia, Francisco and Prats, Jos{\'e} Manuel and G{\"o}tz, Stefan and Kjeldahl, Karin and Montaner, David and Dopazo, Joaquin} } @article {593, title = {GEPAS, a web-based tool for microarray data analysis and interpretation.}, journal = {Nucleic Acids Res}, volume = {36}, year = {2008}, month = {2008 Jul 01}, pages = {W308-14}, abstract = {

Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org.

}, keywords = {Computer Graphics, Dose-Response Relationship, Drug, Gene Expression Profiling, Internet, Kinetics, Oligonucleotide Array Sequence Analysis, Software}, issn = {1362-4962}, doi = {10.1093/nar/gkn303}, author = {T{\'a}rraga, Joaqu{\'\i}n and Medina, Ignacio and Carbonell, Jos{\'e} and Huerta-Cepas, Jaime and Minguez, Pablo and Alloza, Eva and Al-Shahrour, F{\'a}tima and Vegas-Azc{\'a}rate, Susana and Goetz, Stefan and Escobar, Pablo and Garcia-Garcia, Francisco and Conesa, Ana and Montaner, David and Dopazo, Joaquin} } @article {604, title = {Evidence for systems-level molecular mechanisms of tumorigenesis.}, journal = {BMC Genomics}, volume = {8}, year = {2007}, month = {2007 Jun 20}, pages = {185}, abstract = {

BACKGROUND: Cancer arises from the consecutive acquisition of genetic alterations. Increasing evidence suggests that as a consequence of these alterations, molecular interactions are reprogrammed in the context of highly connected and regulated cellular networks. Coordinated reprogramming would allow the cell to acquire the capabilities for malignant growth.

RESULTS: Here, we determine the coordinated function of cancer gene products (i.e., proteins encoded by differentially expressed genes in tumors relative to healthy tissue counterparts, hereafter referred to as "CGPs") defined as their topological properties and organization in the interactome network. We show that CGPs are central to information exchange and propagation and that they are specifically organized to promote tumorigenesis. Centrality is identified by both local (degree) and global (betweenness and closeness) measures, and systematically appears in down-regulated CGPs. Up-regulated CGPs do not consistently exhibit centrality, but both types of cancer products determine the overall integrity of the network structure. In addition to centrality, down-regulated CGPs show topological association that correlates with common biological processes and pathways involved in tumorigenesis.

CONCLUSION: Given the current limited coverage of the human interactome, this study proposes that tumorigenesis takes place in a specific and organized way at the molecular systems-level and suggests a model that comprises the precise down-regulation of groups of topologically-associated proteins involved in particular functions, orchestrated with the up-regulation of specific proteins.

}, keywords = {Cell Transformation, Neoplastic, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Humans, Male, Models, Biological, Models, Genetic, Models, Statistical, Neoplasm Proteins, Neoplasms, Prostatic Neoplasms, Protein Interaction Mapping, RNA, Messenger, Signal Transduction, Systems biology}, issn = {1471-2164}, doi = {10.1186/1471-2164-8-185}, author = {Hern{\'a}ndez, Pilar and Huerta-Cepas, Jaime and Montaner, David and Al-Shahrour, F{\'a}tima and Valls, Joan and G{\'o}mez, Laia and Capell{\`a}, Gabriel and Dopazo, Joaquin and Pujana, Miguel Angel} } @article {605, title = {FatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments.}, journal = {Nucleic Acids Res}, volume = {35}, year = {2007}, month = {2007 Jul}, pages = {W91-6}, abstract = {

The ultimate goal of any genome-scale experiment is to provide a functional interpretation of the data, relating the available information with the hypotheses that originated the experiment. Thus, functional profiling methods have become essential in diverse scenarios such as microarray experiments, proteomics, etc. We present the FatiGO+, a web-based tool for the functional profiling of genome-scale experiments, specially oriented to the interpretation of microarray experiments. In addition to different functional annotations (gene ontology, KEGG pathways, Interpro motifs, Swissprot keywords and text-mining based bioentities related to diseases and chemical compounds) FatiGO+ includes, as a novelty, regulatory and structural information. The regulatory information used includes predictions of targets for distinct regulatory elements (obtained from the Transfac and CisRed databases). Additionally FatiGO+ uses predictions of target motifs of miRNA to infer which of these can be activated or deactivated in the sample of genes studied. Finally, properties of gene products related to their relative location and connections in the interactome have also been used. Also, enrichment of any of these functional terms can be directly analysed on chromosomal coordinates. FatiGO+ can be found at: http://www.fatigoplus.org and within the Babelomics environment http://www.babelomics.org.

}, keywords = {Amino Acid Motifs, Animals, Binding Sites, Computational Biology, Gene Expression Profiling, Genes, Genomics, Humans, Internet, Oligonucleotide Array Sequence Analysis, Programming Languages, Software, Systems Integration, Transcription Factors}, issn = {1362-4962}, doi = {10.1093/nar/gkm260}, author = {Al-Shahrour, F{\'a}tima and Minguez, Pablo and T{\'a}rraga, Joaqu{\'\i}n and Medina, Ignacio and Alloza, Eva and Montaner, David and Dopazo, Joaquin} } @article {606, title = {From genes to functional classes in the study of biological systems.}, journal = {BMC Bioinformatics}, volume = {8}, year = {2007}, month = {2007 Apr 03}, pages = {114}, abstract = {

BACKGROUND: With the popularization of high-throughput techniques, the need for procedures that help in the biological interpretation of results has increased enormously. Recently, new procedures inspired in systems biology criteria have started to be developed.

RESULTS: Here we present FatiScan, a web-based program which implements a threshold-independent test for the functional interpretation of large-scale experiments that does not depend on the pre-selection of genes based on the multiple application of independent tests to each gene. The test implemented aims to directly test the behaviour of blocks of functionally related genes, instead of focusing on single genes. In addition, the test does not depend on the type of the data used for obtaining significance values, and consequently different types of biologically informative terms (gene ontology, pathways, functional motifs, transcription factor binding sites or regulatory sites from CisRed) can be applied to different classes of genome-scale studies. We exemplify its application in microarray gene expression, evolution and interactomics.

CONCLUSION: Methods for gene set enrichment which, in addition, are independent from the original data and experimental design constitute a promising alternative for the functional profiling of genome-scale experiments. A web server that performs the test described and other similar ones can be found at: http://www.babelomics.org.

}, keywords = {Algorithms, Chromosome Mapping, Computer Simulation, Gene Expression Profiling, Models, Biological, Multigene Family, Signal Transduction, Software, Systems biology, User-Computer Interface}, issn = {1471-2105}, doi = {10.1186/1471-2105-8-114}, author = {Al-Shahrour, F{\'a}tima and Arbiza, Leonardo and Dopazo, Hern{\'a}n and Huerta-Cepas, Jaime and Minguez, Pablo and Montaner, David and Dopazo, Joaquin} } @article {592, title = {Functional profiling of microarray experiments using text-mining derived bioentities.}, journal = {Bioinformatics}, volume = {23}, year = {2007}, month = {2007 Nov 15}, pages = {3098-9}, abstract = {

MOTIVATION: The increasing use of microarray technologies brought about a parallel demand in methods for the functional interpretation of the results. Beyond the conventional functional annotations for genes, such as gene ontology, pathways, etc. other sources of information are still to be exploited. Text-mining methods allow extracting informative terms (bioentities) with different functional, chemical, clinical, etc. meanings, that can be associated to genes. We show how to use these associations within an appropriate statistical framework and how to apply them through easy-to-use, web-based environments to the functional interpretation of microarray experiments. Functional enrichment and gene set enrichment tests using bioentities are presented.

}, keywords = {Artificial Intelligence, Databases, Protein, Gene Expression Profiling, Information Storage and Retrieval, Natural Language Processing, Proteins, Research Design, Systems Integration}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btm445}, author = {Minguez, Pablo and Al-Shahrour, F{\'a}tima and Montaner, David and Dopazo, Joaquin} } @article {608, title = {ISACGH: a web-based environment for the analysis of Array CGH and gene expression which includes functional profiling.}, journal = {Nucleic Acids Res}, volume = {35}, year = {2007}, month = {2007 Jul}, pages = {W81-5}, abstract = {

We present the ISACGH, a web-based system that allows for the combination of genomic data with gene expression values and provides different options for functional profiling of the regions found. Several visualization options offer a convenient representation of the results. Different efficient methods for accurate estimation of genomic copy number from array-CGH hybridization data have been included in the program. Moreover, the connection to the gene expression analysis package GEPAS allows the use of different facilities for data pre-processing and analysis. A DAS server allows exporting the results to the Ensembl viewer where contextual genomic information can be obtained. The program is freely available at: http://isacgh.bioinfo.cipf.es or within http://www.gepas.org.

}, keywords = {Animals, Cluster Analysis, Computational Biology, Computer Graphics, Gene Expression Profiling, Humans, Internet, Models, Genetic, Nucleic Acid Hybridization, Oligonucleotide Array Sequence Analysis, Programming Languages, Software, Systems Integration, User-Computer Interface}, issn = {1362-4962}, doi = {10.1093/nar/gkm257}, author = {Conde, Lucia and Montaner, David and Burguet-Castell, Jordi and T{\'a}rraga, Joaqu{\'\i}n and Medina, Ignacio and Al-Shahrour, F{\'a}tima and Dopazo, Joaquin} }