@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 {938, title = {Qualimap: evaluating next-generation sequencing alignment data.}, journal = {Bioinformatics (Oxford, England)}, volume = {28}, year = {2012}, month = {2012 Oct 15}, pages = {2678-9}, abstract = {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.}, keywords = {NGS}, issn = {1367-4811}, doi = {10.1093/bioinformatics/bts503}, url = {http://bioinformatics.oxfordjournals.org/content/28/20/2678.long}, author = {Garc{\'\i}a-Alcalde, Fernando and Okonechnikov, Konstantin and Carbonell, Jos{\'e} and Cruz, Luis M and G{\"o}tz, Stefan and Sonia Tarazona and Joaqu{\'\i}n Dopazo and Meyer, Thomas F and Ana Conesa} } @article {21335611, title = {B2G-FAR, a species centered GO annotation repository.}, journal = {Bioinformatics (Oxford, England)}, volume = {27}, number = {7}, year = {2011}, month = {2011 Feb 18}, pages = {919-924}, abstract = {

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.

}, author = {G{\"o}tz, Stefan and Arnold, Roland and Sebasti{\'a}n-Leon, Patricia and Mart{\'\i}n-Rodr{\'\i}guez, Samuel and Tischler, Patrick and Jehl, Marc-Andr{\'e} and Joaqu{\'\i}n Dopazo and Rattei, Thomas and Ana Conesa} } @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 {19906725, title = {SIMAP{\textendash}a comprehensive database of pre-calculated protein sequence similarities, domains, annotations and clusters.}, journal = {Nucleic acids research}, volume = {38}, year = {2010}, month = {2010 Jan}, pages = {D223-6}, abstract = {

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).

}, author = {Rattei, Thomas and Tischler, Patrick and G{\"o}tz, Stefan and Jehl, Marc-Andr{\'e} and Hoser, Jonathan and Arnold, Roland and Ana Conesa and Mewes, Hans-Werner} } @article {586, title = {SNOW, a web-based tool for the statistical analysis of protein-protein interaction networks.}, journal = {Nucleic Acids Res}, volume = {37}, year = {2009}, month = {2009 Jul}, pages = {W109-14}, abstract = {

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{\textquoteright}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.

}, keywords = {Computer Graphics, Data Interpretation, Statistical, Databases, Protein, Humans, Internet, Protein Interaction Mapping, Software}, issn = {1362-4962}, doi = {10.1093/nar/gkp402}, author = {Minguez, Pablo and G{\"o}tz, Stefan and Montaner, David 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 {594, title = {High-throughput functional annotation and data mining with the Blast2GO suite.}, journal = {Nucleic Acids Res}, volume = {36}, year = {2008}, month = {2008 Jun}, pages = {3420-35}, abstract = {

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.

}, keywords = {Animals, Computational Biology, Computer Graphics, Databases, Genetic, Expressed Sequence Tags, Genes, Genomics, Sequence Analysis, DNA, Sequence Analysis, Protein, Software, Vocabulary, Controlled}, issn = {1362-4962}, doi = {10.1093/nar/gkn176}, author = {G{\"o}tz, Stefan and Garc{\'\i}a-G{\'o}mez, Juan Miguel and Terol, Javier and Williams, Tim D and Nagaraj, Shivashankar H and Nueda, Maria Jos{\'e} and Robles, Montserrat and Talon, Manuel and Dopazo, Joaquin and Conesa, Ana} }