02120nas a2200409 4500008004100000022001400041245012500055210006900180260001200249300001300261490000700274520080500281653001501086653002101101653002601122653002301148653003001171653001301201653004201214653001101256653002401267653001301291653001201304653002401316653001301340653001801353653002701371653001301398100003101411700002601442700002501468700002401493700002001517700002201537700002001559856013101579 2021 eng d a1553-735800aA versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways.0 aversatile workflow to integrate RNAseq genomic and transcriptomi c2021 02 ae10087480 v173 a

MIGNON is a workflow for the analysis of RNA-Seq experiments, which not only efficiently manages the estimation of gene expression levels from raw sequencing reads, but also calls genomic variants present in the transcripts analyzed. Moreover, this is the first workflow that provides a framework for the integration of transcriptomic and genomic data based on a mechanistic model of signaling pathway activities that allows a detailed biological interpretation of the results, including a comprehensive functional profiling of cell activity. MIGNON covers the whole process, from reads to signaling circuit activity estimations, using state-of-the-art tools, it is easy to use and it is deployable in different computational environments, allowing an optimized use of the resources available.

10aAlgorithms10aCell Line, Tumor10aComputational Biology10aDatabases, Factual10aGene Expression Profiling10aGenomics10aHigh-Throughput Nucleotide Sequencing10aHumans10aModels, Theoretical10amutation10aRNA-seq10aSignal Transduction10aSoftware10aTranscriptome10awhole exome sequencing10aWorkflow1 aGarrido-Rodriguez, Martín1 aLópez-López, Daniel1 aOrtuno, Francisco, M1 aPeña-Chilet, Maria1 aMuñoz, Eduardo1 aCalzado, Marco, A1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/versatile-workflow-integrate-rna-seq-genomic-and-transcriptomic-data-mechanistic-models02256nas a2200277 4500008004100000022001400041245011400055210006900169260001600238300000700254490000700261520126600268653002601534653004301560653001101603653004201614653002401656653001801680653002401698100002201722700001901744700001801763700003101781700002001812856014601832 2018 eng d a1745-615000aModels of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome.0 aModels of cell signaling uncover molecular mechanisms of highris c2018 Aug 22 a160 v133 a

BACKGROUND: Despite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40-50%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases.

RESULTS: Since signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression.

CONCLUSION: We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker.

REVIEWERS: This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers' comments section.

10aComputational Biology10aGene Expression Regulation, Neoplastic10aHumans10aJNK Mitogen-Activated Protein Kinases10aModels, Theoretical10aNeuroblastoma10aSignal Transduction1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aCubuk, Cankut1 aCarbonell-Caballero, José1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/models-cell-signaling-uncover-molecular-mechanisms-high-risk-neuroblastoma-and-predict-disease-outcome03368nas a2200241 4500008004100000022001400041245008900055210006900144260001600213300000800229490000700237520258700244653002102831653001102852653002902863653002802892653001102920653002402931653001302955100002002968700001702988856012103005 2017 eng d a1752-050900aGraph-theoretical comparison of normal and tumor networks in identifying BRCA genes.0 aGraphtheoretical comparison of normal and tumor networks in iden c2017 Nov 22 a1100 v113 a

BACKGROUND: Identification of driver genes related to certain types of cancer is an important research topic. Several systems biology approaches have been suggested, in particular for the identification of breast cancer (BRCA) related genes. Such approaches usually rely on differential gene expression and/or mutational landscape data. In some cases interaction network data is also integrated to identify cancer-related modules computationally.

RESULTS: We provide a framework for the comparative graph-theoretical analysis of networks integrating the relevant gene expression, mutations, and potein-protein interaction network data. The comparisons involve a graph-theoretical analysis of normal and tumor network pairs across all instances of a given set of breast cancer samples. The network measures under consideration are based on appropriate formulations of various centrality measures: betweenness, clustering coefficients, degree centrality, random walk distances, graph-theoretical distances, and Jaccard index centrality.

CONCLUSIONS: Among all the studied centrality-based graph-theoretical properties, we show that a betweenness-based measure differentiates BRCA genes across all normal versus tumor network pairs, than the rest of the popular centrality-based measures. The AUROC and AUPR values of the gene lists ordered with respect to the measures under study as compared to NCBI BioSystems pathway and the COSMIC database of cancer genes are the largest with the betweenness-based differentiation, followed by the measure based on degree centrality. In order to test the robustness of the suggested measures in prioritizing cancer genes, we further tested the two most promising measures, those based on betweenness and degree centralities, on randomly rewired networks. We show that both measures are quite resilient to noise in the input interaction network. We also compared the same measures against a state-of-the-art alternative disease gene prioritization method, MUFFFINN. We show that both our graph-theoretical measures outperform MUFFINN prioritizations in terms of ROC and precions/recall analysis. Finally, we filter the ordered list of the best measure, the betweenness-based differentiation, via a maximum-weight independent set formulation and investigate the top 50 genes in regards to literature verification. We show that almost all genes in the list are verified by the breast cancer literature and three genes are presented as novel genes that may potentialy be BRCA-related but missing in literature.

10aBreast Neoplasms10aFemale10aGene Regulatory Networks10aGenes, Tumor Suppressor10aHumans10aModels, Theoretical10amutation1 aDopazo, Joaquin1 aErten, Cesim uhttps://www.clinbioinfosspa.es/content/graph-theoretical-comparison-normal-and-tumor-networks-identifying-brca-genes01773nas a2200205 4500008004100000022001400041245006300055210006200118260001700180300001100197490000700208520113100215653002501346653002601371653001301397653001301410653002401423100002001447856010001467 2009 eng d a1873-286000aFormulating and testing hypotheses in functional genomics.0 aFormulating and testing hypotheses in functional genomics c2009 Feb-Mar a97-1070 v453 a

OBJECTIVE: The ultimate goal of any genome-scale experiment is to provide a functional interpretation of the results, relating the available genomic information to the hypotheses that originated the experiment.

METHODS AND RESULTS: Initially, this interpretation has been made on a pre-selection of relevant genes, based on the experimental values, followed by the study of the enrichment in some functional properties. Nevertheless, functional enrichment methods, demonstrated to have a flaw: the first step of gene selection was too stringent given that the cooperation among genes was ignored. The assumption that modules of genes related by relevant biological properties (functionality, co-regulation, chromosomal location, etc.) are the real actors of the cell biology lead to the development of new procedures, inspired in systems biology criteria, generically known as gene-set methods. These methods have been successfully used to analyze transcriptomic and large-scale genotyping experiments as well as to test other different genome-scale hypothesis in other fields such as phylogenomics.

10aBiological Evolution10aComputational Biology10aGenomics10aGenotype10aModels, Theoretical1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/formulating-and-testing-hypotheses-functional-genomics-0