@article {1129, title = {Babelomics 5.0: functional interpretation for new generations of genomic data.}, journal = {Nucleic acids research}, volume = {43}, number = {W1}, year = {2015}, month = {2015 Apr 20}, pages = {W117-W121}, abstract = {Babelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome resequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at: http://www.babelomics.org.}, keywords = {babelomics, data integration, gene set analysis, interactome, network analysis, NGS, RNA-seq, Systems biology, transcriptomics}, issn = {1362-4962}, doi = {10.1093/nar/gkv384}, url = {http://nar.oxfordjournals.org/content/43/W1/W117}, author = {Alonso, Roberto and Salavert, Francisco and Garcia-Garcia, Francisco and Carbonell-Caballero, Jos{\'e} and Bleda, Marta and Garc{\'\i}a-Alonso, Luz and Sanchis-Juan, Alba and Perez-Gil, Daniel and Marin-Garcia, Pablo and S{\'a}nchez, Rub{\'e}n and Cubuk, Cankut and Hidalgo, Marta R and Amadoz, Alicia and Hernansaiz-Ballesteros, Rosa D and Alem{\'a}n, Alejandro and T{\'a}rraga, Joaqu{\'\i}n and Montaner, David and Medina, Ignacio and Dopazo, Joaquin} } @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 {1093, title = {Understanding disease mechanisms with models of signaling pathway activities.}, journal = {BMC systems biology}, volume = {8}, year = {2014}, month = {2014 Oct 25}, pages = {121}, abstract = {BackgroundUnderstanding the aspects of the cell functionality that account for disease or drug action mechanisms is one of the main challenges in the analysis of genomic data and is on the basis of the future implementation of precision medicine.ResultsHere we propose a simple probabilistic model in which signaling pathways are separated into elementary sub-pathways or signal transmission circuits (which ultimately trigger cell functions) and then transforms gene expression measurements into probabilities of activation of such signal transmission circuits. Using this model, differential activation of such circuits between biological conditions can be estimated. Thus, circuit activation statuses can be interpreted as biomarkers that discriminate among the compared conditions. This type of mechanism-based biomarkers accounts for cell functional activities and can easily be associated to disease or drug action mechanisms. The accuracy of the proposed model is demonstrated with simulations and real datasets.ConclusionsThe proposed model provides detailed information that enables the interpretation disease mechanisms as a consequence of the complex combinations of altered gene expression values. Moreover, it offers a framework for suggesting possible ways of therapeutic intervention in a pathologically perturbed system.}, keywords = {Disease mechanism, pathway, signalling, Systems biology}, issn = {1752-0509}, doi = {10.1186/s12918-014-0121-3}, url = {http://www.biomedcentral.com/1752-0509/8/121/abstract}, author = {Sebasti{\'a}n-Leon, Patricia and Vidal, Enrique and Minguez, Pablo and Ana Conesa and Sonia Tarazona and Amadoz, Alicia and Armero, Carmen and Salavert, Francisco and Vidal-Puig, Antonio and Montaner, David and Joaqu{\'\i}n Dopazo} } @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 {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} }