@article {712, title = {A versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways.}, journal = {PLoS Comput Biol}, volume = {17}, year = {2021}, month = {2021 02}, pages = {e1008748}, abstract = {

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.

}, keywords = {Algorithms, Cell Line, Tumor, Computational Biology, Databases, Factual, Gene Expression Profiling, Genomics, High-Throughput Nucleotide Sequencing, Humans, Models, Theoretical, mutation, RNA-seq, Signal Transduction, Software, Transcriptome, whole exome sequencing, Workflow}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1008748}, author = {Garrido-Rodriguez, Mart{\'\i}n and L{\'o}pez-L{\'o}pez, Daniel and Ortuno, Francisco M and Pe{\~n}a-Chilet, Maria and Mu{\~n}oz, Eduardo and Calzado, Marco A and Dopazo, Joaquin} } @article {404, title = {Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome.}, journal = {Biol Direct}, volume = {13}, year = {2018}, month = {2018 Aug 22}, pages = {16}, abstract = {

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{\textquoteright} comments section.

}, keywords = {Computational Biology, Gene Expression Regulation, Neoplastic, Humans, JNK Mitogen-Activated Protein Kinases, Models, Theoretical, Neuroblastoma, Signal Transduction}, issn = {1745-6150}, doi = {10.1186/s13062-018-0219-4}, author = {Hidalgo, Marta R and Amadoz, Alicia and Cubuk, Cankut and Carbonell-Caballero, Jos{\'e} and Dopazo, Joaquin} } @article {399, title = {Graph-theoretical comparison of normal and tumor networks in identifying BRCA genes.}, journal = {BMC Syst Biol}, volume = {11}, year = {2017}, month = {2017 Nov 22}, pages = {110}, abstract = {

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.

}, keywords = {Breast Neoplasms, Female, Gene Regulatory Networks, Genes, Tumor Suppressor, Humans, Models, Theoretical, mutation}, issn = {1752-0509}, doi = {10.1186/s12918-017-0495-0}, author = {Dopazo, Joaquin and Erten, Cesim} } @article {579, title = {Formulating and testing hypotheses in functional genomics.}, journal = {Artif Intell Med}, volume = {45}, year = {2009}, month = {2009 Feb-Mar}, pages = {97-107}, abstract = {

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.

}, keywords = {Biological Evolution, Computational Biology, Genomics, Genotype, Models, Theoretical}, issn = {1873-2860}, doi = {10.1016/j.artmed.2008.08.003}, author = {Dopazo, Joaquin} }