TY - JOUR T1 - Pathway network inference from gene expression data. JF - BMC Syst Biol Y1 - 2014 A1 - Ponzoni, Ignacio A1 - Nueda, María A1 - Tarazona, Sonia A1 - Götz, Stefan A1 - Montaner, David A1 - Dussaut, Julieta A1 - Dopazo, Joaquin A1 - Conesa, Ana KW - Alzheimer Disease KW - Cell Cycle KW - DNA Replication KW - Gene Expression Profiling KW - Gene Regulatory Networks KW - Gluconeogenesis KW - Glycolysis KW - Oxidative Phosphorylation KW - Proteolysis KW - Purines KW - Saccharomyces cerevisiae KW - Systems biology KW - Ubiquitin AB -

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's topology obtained for the yeast cell cycle data.

VL - 8 Suppl 2 U1 - https://www.ncbi.nlm.nih.gov/pubmed/25032889?dopt=Abstract ER - TY - JOUR T1 - Qualimap: evaluating next-generation sequencing alignment data. JF - Bioinformatics (Oxford, England) Y1 - 2012 A1 - García-Alcalde, Fernando A1 - Okonechnikov, Konstantin A1 - Carbonell, José A1 - Cruz, Luis M A1 - Götz, Stefan A1 - Sonia Tarazona A1 - Joaquín Dopazo A1 - Meyer, Thomas F A1 - Ana Conesa KW - NGS AB - MOTIVATION: The sequence alignment/map (SAM) and the binary alignment/map (BAM) formats have become the standard method of representation of nucleotide sequence alignments for next-generation sequencing data. SAM/BAM files usually contain information from tens to hundreds of millions of reads. Often, the sequencing technology, protocol and/or the selected mapping algorithm introduce some unwanted biases in these data. The systematic detection of such biases is a non-trivial task that is crucial to drive appropriate downstream analyses. RESULTS: We have developed Qualimap, a Java application that supports user-friendly quality control of mapping data, by considering sequence features and their genomic properties. Qualimap takes sequence alignment data and provides graphical and statistical analyses for the evaluation of data. Such quality-control data are vital for highlighting problems in the sequencing and/or mapping processes, which must be addressed prior to further analyses. AVAILABILITY: Qualimap is freely available from http://www.qualimap.org. CONTACT: aconesa@cipf.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. VL - 28 UR - http://bioinformatics.oxfordjournals.org/content/28/20/2678.long ER - TY - JOUR T1 - B2G-FAR, a species centered GO annotation repository. JF - Bioinformatics (Oxford, England) Y1 - 2011 A1 - Götz, Stefan A1 - Arnold, Roland A1 - Sebastián-Leon, Patricia A1 - Martín-Rodríguez, Samuel A1 - Tischler, Patrick A1 - Jehl, Marc-André A1 - Joaquín Dopazo A1 - Rattei, Thomas A1 - Ana Conesa AB -

MOTIVATION: Functional genomics research has expanded enormously in the last decade thanks to the cost-reduction in high-throughput technologies and the development of computational tools that generate, standardize and share information on gene and protein function such as the Gene Ontology (GO). Nevertheless many biologists, especially working with non-model organisms, still suffer from non-existing or low coverage functional annotation, or simply struggle retrieving, summarizing and querying these data. RESULTS: The Blast2GO Functional Annotation Repository (B2G-FAR) is a bioinformatics resource envisaged to provide functional information for otherwise uncharacterized sequence-data and offers data-mining tools to analyze a larger repertoire of species than currently available. This new annotation resource has been created by applying the Blast2GO functional annotation engine in a strongly high-throughput manner to the entire space of public available sequences. The resulting repository contains GO term predictions for over 13.2 million non-redundant protein sequences based on BLAST search alignments from the SIMAP database. We generated GO annotation for approximately 150.000 different taxa making available the 2000 species with the highest coverage through B2G-FAR. A second section within B2G-FAR holds functional annotations for 17 non-model organism Affymetrix GeneChips. Conclusions: B2G-FAR provides easy access to exhaustive functional annotation for 2000 species offering a good balance between quality and quantity, thereby supporting functional genomics research especially in the case of non-model organisms. AVAILABILITY: The annotation resource is available at http://b2gfar.bioinfo.cipf.es. CONTACT: aconesa@cipf.es, sgoetz@cipf.es.

VL - 27 ER - TY - JOUR T1 - Hypoxia promotes efficient differentiation of human embryonic stem cells to functional endothelium. JF - Stem Cells Y1 - 2010 A1 - Prado-Lopez, Sonia A1 - Conesa, Ana A1 - Armiñán, Ana A1 - Martínez-Losa, Magdalena A1 - Escobedo-Lucea, Carmen A1 - Gandia, Carolina A1 - Tarazona, Sonia A1 - Melguizo, Dario A1 - Blesa, David A1 - Montaner, David A1 - Sanz-González, Silvia A1 - Sepúlveda, Pilar A1 - Götz, Stefan A1 - O'Connor, José Enrique A1 - Moreno, Ruben A1 - Dopazo, Joaquin A1 - Burks, Deborah J A1 - Stojkovic, Miodrag KW - Angiopoietin-1 KW - Animals KW - biomarkers KW - Cell Culture Techniques KW - Cell Differentiation KW - Cell Hypoxia KW - Cell Transplantation KW - Cells, Cultured KW - Down-Regulation KW - Embryonic Stem Cells KW - Endothelial Cells KW - Gene Expression Profiling KW - Gene Expression Regulation KW - Humans KW - Male KW - Myocardial Infarction KW - Neovascularization, Physiologic KW - Oxygen KW - Pluripotent Stem Cells KW - Rats KW - Rats, Nude KW - Vascular Endothelial Growth Factor A AB -

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.

VL - 28 IS - 3 U1 - https://www.ncbi.nlm.nih.gov/pubmed/20049902?dopt=Abstract ER - TY - JOUR T1 - SIMAP–a comprehensive database of pre-calculated protein sequence similarities, domains, annotations and clusters. JF - Nucleic acids research Y1 - 2010 A1 - Rattei, Thomas A1 - Tischler, Patrick A1 - Götz, Stefan A1 - Jehl, Marc-André A1 - Hoser, Jonathan A1 - Arnold, Roland A1 - Ana Conesa A1 - Mewes, Hans-Werner AB -

The prediction of protein function as well as the reconstruction of evolutionary genesis employing sequence comparison at large is still the most powerful tool in sequence analysis. Due to the exponential growth of the number of known protein sequences and the subsequent quadratic growth of the similarity matrix, the computation of the Similarity Matrix of Proteins (SIMAP) becomes a computational intensive task. The SIMAP database provides a comprehensive and up-to-date pre-calculation of the protein sequence similarity matrix, sequence-based features and sequence clusters. As of September 2009, SIMAP covers 48 million proteins and more than 23 million non-redundant sequences. Novel features of SIMAP include the expansion of the sequence space by including databases such as ENSEMBL as well as the integration of metagenomes based on their consistent processing and annotation. Furthermore, protein function predictions by Blast2GO are pre-calculated for all sequences in SIMAP and the data access and query functions have been improved. SIMAP assists biologists to query the up-to-date sequence space systematically and facilitates large-scale downstream projects in computational biology. Access to SIMAP is freely provided through the web portal for individuals (http://mips.gsf.de/simap/) and for programmatic access through DAS (http://webclu.bio.wzw.tum.de/das/) and Web-Service (http://mips.gsf.de/webservices/services/SimapService2.0?wsdl).

VL - 38 ER - TY - JOUR T1 - SNOW, a web-based tool for the statistical analysis of protein-protein interaction networks. JF - Nucleic Acids Res Y1 - 2009 A1 - Minguez, Pablo A1 - Götz, Stefan A1 - Montaner, David A1 - Al-Shahrour, Fátima A1 - Dopazo, Joaquin KW - Computer Graphics KW - Data Interpretation, Statistical KW - Databases, Protein KW - Humans KW - Internet KW - Protein Interaction Mapping KW - Software AB -

Understanding the structure and the dynamics of the complex intercellular network of interactions that contributes to the structure and function of a living cell is one of the main challenges of today's biology. SNOW inputs a collection of protein (or gene) identifiers and, by using the interactome as scaffold, draws the connections among them, calculates several relevant network parameters and, as a novelty among the rest of tools of its class, it estimates their statistical significance. The parameters calculated for each node are: connectivity, betweenness and clustering coefficient. It also calculates the number of components, number of bicomponents and articulation points. An interactive network viewer is also available to explore the resulting network. SNOW is available at http://snow.bioinfo.cipf.es.

VL - 37 IS - Web Server issue U1 - https://www.ncbi.nlm.nih.gov/pubmed/19454602?dopt=Abstract ER - TY - JOUR T1 - Direct functional assessment of the composite phenotype through multivariate projection strategies. JF - Genomics Y1 - 2008 A1 - Conesa, Ana A1 - Bro, Rasmus A1 - Garcia-Garcia, Francisco A1 - Prats, José Manuel A1 - Götz, Stefan A1 - Kjeldahl, Karin A1 - Montaner, David A1 - Dopazo, Joaquin KW - Breast Neoplasms KW - Computational Biology KW - Databases, Genetic KW - Female KW - Gene Expression Profiling KW - Humans KW - Mathematical Computing KW - Multivariate Analysis KW - Phenotype AB -

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.

VL - 92 IS - 6 U1 - https://www.ncbi.nlm.nih.gov/pubmed/18652888?dopt=Abstract ER - TY - JOUR T1 - High-throughput functional annotation and data mining with the Blast2GO suite. JF - Nucleic Acids Res Y1 - 2008 A1 - Götz, Stefan A1 - García-Gómez, Juan Miguel A1 - Terol, Javier A1 - Williams, Tim D A1 - Nagaraj, Shivashankar H A1 - Nueda, Maria José A1 - Robles, Montserrat A1 - Talon, Manuel A1 - Dopazo, Joaquin A1 - Conesa, Ana KW - Animals KW - Computational Biology KW - Computer Graphics KW - Databases, Genetic KW - Expressed Sequence Tags KW - Genes KW - Genomics KW - Sequence Analysis, DNA KW - Sequence Analysis, Protein KW - Software KW - Vocabulary, Controlled AB -

Functional genomics technologies have been widely adopted in the biological research of both model and non-model species. An efficient functional annotation of DNA or protein sequences is a major requirement for the successful application of these approaches as functional information on gene products is often the key to the interpretation of experimental results. Therefore, there is an increasing need for bioinformatics resources which are able to cope with large amount of sequence data, produce valuable annotation results and are easily accessible to laboratories where functional genomics projects are being undertaken. We present the Blast2GO suite as an integrated and biologist-oriented solution for the high-throughput and automatic functional annotation of DNA or protein sequences based on the Gene Ontology vocabulary. The most outstanding Blast2GO features are: (i) the combination of various annotation strategies and tools controlling type and intensity of annotation, (ii) the numerous graphical features such as the interactive GO-graph visualization for gene-set function profiling or descriptive charts, (iii) the general sequence management features and (iv) high-throughput capabilities. We used the Blast2GO framework to carry out a detailed analysis of annotation behaviour through homology transfer and its impact in functional genomics research. Our aim is to offer biologists useful information to take into account when addressing the task of functionally characterizing their sequence data.

VL - 36 IS - 10 U1 - https://www.ncbi.nlm.nih.gov/pubmed/18445632?dopt=Abstract ER -