@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 {529, title = {Differential expression in RNA-seq: a matter of depth.}, journal = {Genome Res}, volume = {21}, year = {2011}, month = {2011 Dec}, pages = {2213-23}, abstract = {

Next-generation sequencing (NGS) technologies are revolutionizing genome research, and in particular, their application to transcriptomics (RNA-seq) is increasingly being used for gene expression profiling as a replacement for microarrays. However, the properties of RNA-seq data have not been yet fully established, and additional research is needed for understanding how these data respond to differential expression analysis. In this work, we set out to gain insights into the characteristics of RNA-seq data analysis by studying an important parameter of this technology: the sequencing depth. We have analyzed how sequencing depth affects the detection of transcripts and their identification as differentially expressed, looking at aspects such as transcript biotype, length, expression level, and fold-change. We have evaluated different algorithms available for the analysis of RNA-seq and proposed a novel approach--NOISeq--that differs from existing methods in that it is data-adaptive and nonparametric. Our results reveal that most existing methodologies suffer from a strong dependency on sequencing depth for their differential expression calls and that this results in a considerable number of false positives that increases as the number of reads grows. In contrast, our proposed method models the noise distribution from the actual data, can therefore better adapt to the size of the data set, and is more effective in controlling the rate of false discoveries. This work discusses the true potential of RNA-seq for studying regulation at low expression ranges, the noise within RNA-seq data, and the issue of replication.

}, keywords = {Algorithms, Expressed Sequence Tags, Gene Expression Profiling, Gene Expression Regulation, Humans, Models, Genetic, Oligonucleotide Array Sequence Analysis}, issn = {1549-5469}, doi = {10.1101/gr.124321.111}, author = {Tarazona, Sonia and Garc{\'\i}a-Alcalde, Fernando and Dopazo, Joaquin and Ferrer, Alberto and Conesa, Ana} } @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 {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 {576, title = {Serial Expression Analysis: a web tool for the analysis of serial gene expression data.}, journal = {Nucleic Acids Res}, volume = {38}, year = {2010}, month = {2010 Jul}, pages = {W239-45}, abstract = {

Serial transcriptomics experiments investigate the dynamics of gene expression changes associated with a quantitative variable such as time or dosage. The statistical analysis of these data implies the study of global and gene-specific expression trends, the identification of significant serial changes, the comparison of expression profiles and the assessment of transcriptional changes in terms of cellular processes. We have created the SEA (Serial Expression Analysis) suite to provide a complete web-based resource for the analysis of serial transcriptomics data. SEA offers five different algorithms based on univariate, multivariate and functional profiling strategies framed within a user-friendly interface and a project-oriented architecture to facilitate the analysis of serial gene expression data sets from different perspectives. SEA is available at sea.bioinfo.cipf.es.

}, keywords = {Algorithms, Gene Expression Profiling, Internet, Kinetics, Linear Models, Oligonucleotide Array Sequence Analysis, Software}, issn = {1362-4962}, doi = {10.1093/nar/gkq488}, author = {Nueda, Maria Jos{\'e} and Carbonell, Jos{\'e} and Medina, Ignacio and Dopazo, Joaquin and Conesa, Ana} } @article {580, title = {Functional assessment of time course microarray data.}, journal = {BMC Bioinformatics}, volume = {10 Suppl 6}, year = {2009}, month = {2009 Jun 16}, pages = {S9}, abstract = {

MOTIVATION: Time-course microarray experiments study the progress of gene expression along time across one or several experimental conditions. Most developed analysis methods focus on the clustering or the differential expression analysis of genes and do not integrate functional information. The assessment of the functional aspects of time-course transcriptomics data requires the use of approaches that exploit the activation dynamics of the functional categories to where genes are annotated.

METHODS: We present three novel methodologies for the functional assessment of time-course microarray data. i) maSigFun derives from the maSigPro method, a regression-based strategy to model time-dependent expression patterns and identify genes with differences across series. maSigFun fits a regression model for groups of genes labeled by a functional class and selects those categories which have a significant model. ii) PCA-maSigFun fits a PCA model of each functional class-defined expression matrix to extract orthogonal patterns of expression change, which are then assessed for their fit to a time-dependent regression model. iii) ASCA-functional uses the ASCA model to rank genes according to their correlation to principal time expression patterns and assess functional enrichment on a GSA fashion. We used simulated and experimental datasets to study these novel approaches. Results were compared to alternative methodologies.

RESULTS: Synthetic and experimental data showed that the different methods are able to capture different aspects of the relationship between genes, functions and co-expression that are biologically meaningful. The methods should not be considered as competitive but they provide different insights into the molecular and functional dynamic events taking place within the biological system under study.

}, keywords = {Computer Simulation, Gene Expression Profiling, Oligonucleotide Array Sequence Analysis, Time Factors}, issn = {1471-2105}, doi = {10.1186/1471-2105-10-S6-S9}, author = {Nueda, Maria Jos{\'e} and Sebasti{\'a}n, Patricia and Tarazona, Sonia and Garcia-Garcia, Francisco and Dopazo, Joaquin and Ferrer, Alberto and Conesa, Ana} } @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} }