<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maillo, Alberto</style></author><author><style face="normal" font="default" size="100%">Huergo, Estefania</style></author><author><style face="normal" font="default" size="100%">Apellániz-Ruiz, María</style></author><author><style face="normal" font="default" size="100%">Urrutia-Lafuente, Edurne</style></author><author><style face="normal" font="default" size="100%">Miranda, María</style></author><author><style face="normal" font="default" size="100%">Salgado, Josefa</style></author><author><style face="normal" font="default" size="100%">Pasalodos-Sanchez, Sara</style></author><author><style face="normal" font="default" size="100%">Delgado-Mora, Luna</style></author><author><style face="normal" font="default" size="100%">Teijido, Óscar</style></author><author><style face="normal" font="default" size="100%">Goicoechea, Ibai</style></author><author><style face="normal" font="default" size="100%">Carmona, Rosario</style></author><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">Aquino, Virginia</style></author><author><style face="normal" font="default" size="100%">López-López, Daniel</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Beltran, Sergi</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Lasa, Iñigo</style></author><author><style face="normal" font="default" size="100%">Beloqui, Juan José</style></author><author><style face="normal" font="default" size="100%">Alonso, Ángel</style></author><author><style face="normal" font="default" size="100%">Gomez-Cabrero, David</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Characterization of the Common Genetic Variation in the Spanish Population of Navarre.</style></title><secondary-title><style face="normal" font="default" size="100%">Genes (Basel)</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genes (Basel)</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cohort Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Frequency</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetics, Population</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Spain</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Genome Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 May 04</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">15</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Large-scale genomic studies have significantly increased our knowledge of genetic variability across populations. Regional genetic profiling is essential for distinguishing common benign variants from disease-causing ones. To this end, we conducted a comprehensive characterization of exonic variants in the population of Navarre (Spain), utilizing whole genome sequencing data from 358 unrelated individuals of Spanish origin. Our analysis revealed 61,410 biallelic single nucleotide variants (SNV) within the Navarrese cohort, with 35% classified as common (MAF &gt; 1%). By comparing allele frequency data from 1000 Genome Project (excluding the Iberian cohort of Spain, IBS), Genome Aggregation Database, and a Spanish cohort (including IBS individuals and data from Medical Genome Project), we identified 1069 SNVs common in Navarre but rare (MAF ≤ 1%) in all other populations. We further corroborated this observation with a second regional cohort of 239 unrelated exomes, which confirmed 676 of the 1069 SNVs as common in Navarre. In conclusion, this study highlights the importance of population-specific characterization of genetic variation to improve allele frequency filtering in sequencing data analysis to identify disease-causing variants.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gundogdu, Pelin</style></author><author><style face="normal" font="default" size="100%">Payá-Milans, Miriam</style></author><author><style face="normal" font="default" size="100%">Alamo-Alvarez, Inmaculada</style></author><author><style face="normal" font="default" size="100%">Nepomuceno-Chamorro, Isabel A.</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cell-Level Pathway Scoring Comparison with a Biologically Constrained Variational Autoencoder</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science. Computational Methods in Systems Biology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-031-42697-1_5</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer Nature Switzerland</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><volume><style face="normal" font="default" size="100%">14137</style></volume><pages><style face="normal" font="default" size="100%">62 - 77</style></pages><isbn><style face="normal" font="default" size="100%">978-3-031-42696-4</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Núñez-Torres, Rocío</style></author><author><style face="normal" font="default" size="100%">Pita, Guillermo</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">López-López, Daniel</style></author><author><style face="normal" font="default" size="100%">Zamora, Jorge</style></author><author><style face="normal" font="default" size="100%">Roldán, Gema</style></author><author><style face="normal" font="default" size="100%">Herráez, Belén</style></author><author><style face="normal" font="default" size="100%">Alvarez, Nuria</style></author><author><style face="normal" font="default" size="100%">Alonso, María Rosario</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">González-Neira, Anna</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Comprehensive Analysis of 21 Actionable Pharmacogenes in the Spanish Population: From Genetic Characterisation to Clinical Impact.</style></title><secondary-title><style face="normal" font="default" size="100%">Pharmaceutics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Pharmaceutics</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 Apr 19</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">15</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The implementation of pharmacogenetics (PGx) is a main milestones of precision medicine nowadays in order to achieve safer and more effective therapies. Nevertheless, the implementation of PGx diagnostics is extremely slow and unequal worldwide, in part due to a lack of ethnic PGx information. We analysed genetic data from 3006 Spanish individuals obtained by different high-throughput (HT) techniques. Allele frequencies were determined in our population for the main 21 actionable PGx genes associated with therapeutical changes. We found that 98% of the Spanish population harbours at least one allele associated with a therapeutical change and, thus, there would be a need for a therapeutical change in a mean of 3.31 of the 64 associated drugs. We also identified 326 putative deleterious variants that were not previously related with PGx in 18 out of the 21 main PGx genes evaluated and a total of 7122 putative deleterious variants for the 1045 PGx genes described. Additionally, we performed a comparison of the main HT diagnostic techniques, revealing that after whole genome sequencing, genotyping with the PGx HT array is the most suitable solution for PGx diagnostics. Finally, all this information was integrated in the Collaborative Spanish Variant Server to be available to and updated by the scientific community.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Crosstalk between Metabolite Production and Signaling Activity in Breast Cancer.</style></title><secondary-title><style face="normal" font="default" size="100%">Int J Mol Sci</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Int J Mol Sci</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 Apr 18</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">24</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The reprogramming of metabolism is a recognized cancer hallmark. It is well known that different signaling pathways regulate and orchestrate this reprogramming that contributes to cancer initiation and development. However, recent evidence is accumulating, suggesting that several metabolites could play a relevant role in regulating signaling pathways. To assess the potential role of metabolites in the regulation of signaling pathways, both metabolic and signaling pathway activities of Breast invasive Carcinoma (BRCA) have been modeled using mechanistic models. Gaussian Processes, powerful machine learning methods, were used in combination with SHapley Additive exPlanations (SHAP), a recent methodology that conveys causality, to obtain potential causal relationships between the production of metabolites and the regulation of signaling pathways. A total of 317 metabolites were found to have a strong impact on signaling circuits. The results presented here point to the existence of a complex crosstalk between signaling and metabolic pathways more complex than previously was thought.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">8</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">López-López, Daniel</style></author><author><style face="normal" font="default" size="100%">Roldán, Gema</style></author><author><style face="normal" font="default" size="100%">Fernandez-Rueda, Jose L</style></author><author><style face="normal" font="default" size="100%">Bostelmann, Gerrit</style></author><author><style face="normal" font="default" size="100%">Carmona, Rosario</style></author><author><style face="normal" font="default" size="100%">Aquino, Virginia</style></author><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">Ortuno, Francisco</style></author><author><style face="normal" font="default" size="100%">Pita, Guillermo</style></author><author><style face="normal" font="default" size="100%">Núñez-Torres, Rocío</style></author><author><style face="normal" font="default" size="100%">González-Neira, Anna</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">CSVS Crowdsourcing Group</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">A crowdsourcing database for the copy-number variation of the Spanish population.</style></title><secondary-title><style face="normal" font="default" size="100%">Hum Genomics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Hum Genomics</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 Mar 09</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">20</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Despite being a very common type of genetic variation, the distribution of copy-number variations (CNVs) in the population is still poorly understood. The knowledge of the genetic variability, especially at the level of the local population, is a critical factor for distinguishing pathogenic from non-pathogenic variation in the discovery of new disease variants.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Here, we present the SPAnish Copy Number Alterations Collaborative Server (SPACNACS), which currently contains copy number variation profiles obtained from more than 400 genomes and exomes of unrelated Spanish individuals. By means of a collaborative crowdsourcing effort whole genome and whole exome sequencing data, produced by local genomic projects and for other purposes, is continuously collected. Once checked both, the Spanish ancestry and the lack of kinship with other individuals in the SPACNACS, the CNVs are inferred for these sequences and they are used to populate the database. A web interface allows querying the database with different filters that include ICD10 upper categories. This allows discarding samples from the disease under study and obtaining pseudo-control CNV profiles from the local population. We also show here additional studies on the local impact of CNVs in some phenotypes and on pharmacogenomic variants. SPACNACS can be accessed at: http://csvs.clinbioinfosspa.es/spacnacs/ .&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;SPACNACS facilitates disease gene discovery by providing detailed information of the local variability of the population and exemplifies how to reuse genomic data produced for other purposes to build a local reference database.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martorell-Marugán, Jordi</style></author><author><style face="normal" font="default" size="100%">López-Domínguez, Raúl</style></author><author><style face="normal" font="default" size="100%">García-Moreno, Adrián</style></author><author><style face="normal" font="default" size="100%">Toro-Domínguez, Daniel</style></author><author><style face="normal" font="default" size="100%">Villatoro-García, Juan Antonio</style></author><author><style face="normal" font="default" size="100%">Barturen, Guillermo</style></author><author><style face="normal" font="default" size="100%">Martín-Gómez, Adoración</style></author><author><style face="normal" font="default" size="100%">Troule, Kevin</style></author><author><style face="normal" font="default" size="100%">Gómez-López, Gonzalo</style></author><author><style face="normal" font="default" size="100%">Al-Shahrour, Fátima</style></author><author><style face="normal" font="default" size="100%">González-Rumayor, Víctor</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Saez-Rodriguez, Julio</style></author><author><style face="normal" font="default" size="100%">Alarcón-Riquelme, Marta E</style></author><author><style face="normal" font="default" size="100%">Carmona-Sáez, Pedro</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A comprehensive database for integrated analysis of omics data in autoimmune diseases.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Autoimmune Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Factual</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 Jun 24</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">343</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Here, we present Autoimmune Diseases Explorer ( https://adex.genyo.es ), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/34167460?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ostaszewski, Marek</style></author><author><style face="normal" font="default" size="100%">Niarakis, Anna</style></author><author><style face="normal" font="default" size="100%">Mazein, Alexander</style></author><author><style face="normal" font="default" size="100%">Kuperstein, Inna</style></author><author><style face="normal" font="default" size="100%">Phair, Robert</style></author><author><style face="normal" font="default" size="100%">Orta-Resendiz, Aurelio</style></author><author><style face="normal" font="default" size="100%">Singh, Vidisha</style></author><author><style face="normal" font="default" size="100%">Aghamiri, Sara Sadat</style></author><author><style face="normal" font="default" size="100%">Acencio, Marcio Luis</style></author><author><style face="normal" font="default" size="100%">Glaab, Enrico</style></author><author><style face="normal" font="default" size="100%">Ruepp, Andreas</style></author><author><style face="normal" font="default" size="100%">Fobo, Gisela</style></author><author><style face="normal" font="default" size="100%">Montrone, Corinna</style></author><author><style face="normal" font="default" size="100%">Brauner, Barbara</style></author><author><style face="normal" font="default" size="100%">Frishman, Goar</style></author><author><style face="normal" font="default" size="100%">Monraz Gómez, Luis Cristóbal</style></author><author><style face="normal" font="default" size="100%">Somers, Julia</style></author><author><style face="normal" font="default" size="100%">Hoch, Matti</style></author><author><style face="normal" font="default" size="100%">Kumar Gupta, Shailendra</style></author><author><style face="normal" font="default" size="100%">Scheel, Julia</style></author><author><style face="normal" font="default" size="100%">Borlinghaus, Hanna</style></author><author><style face="normal" font="default" size="100%">Czauderna, Tobias</style></author><author><style face="normal" font="default" size="100%">Schreiber, Falk</style></author><author><style face="normal" font="default" size="100%">Montagud, Arnau</style></author><author><style face="normal" font="default" size="100%">Ponce de Leon, Miguel</style></author><author><style face="normal" font="default" size="100%">Funahashi, Akira</style></author><author><style face="normal" font="default" size="100%">Hiki, Yusuke</style></author><author><style face="normal" font="default" size="100%">Hiroi, Noriko</style></author><author><style face="normal" font="default" size="100%">Yamada, Takahiro G</style></author><author><style face="normal" font="default" size="100%">Dräger, Andreas</style></author><author><style face="normal" font="default" size="100%">Renz, Alina</style></author><author><style face="normal" font="default" size="100%">Naveez, Muhammad</style></author><author><style face="normal" font="default" size="100%">Bocskei, Zsolt</style></author><author><style face="normal" font="default" size="100%">Messina, Francesco</style></author><author><style face="normal" font="default" size="100%">Börnigen, Daniela</style></author><author><style face="normal" font="default" size="100%">Fergusson, Liam</style></author><author><style face="normal" font="default" size="100%">Conti, Marta</style></author><author><style face="normal" font="default" size="100%">Rameil, Marius</style></author><author><style face="normal" font="default" size="100%">Nakonecnij, Vanessa</style></author><author><style face="normal" font="default" size="100%">Vanhoefer, Jakob</style></author><author><style face="normal" font="default" size="100%">Schmiester, Leonard</style></author><author><style face="normal" font="default" size="100%">Wang, Muying</style></author><author><style face="normal" font="default" size="100%">Ackerman, Emily E</style></author><author><style face="normal" font="default" size="100%">Shoemaker, Jason E</style></author><author><style face="normal" font="default" size="100%">Zucker, Jeremy</style></author><author><style face="normal" font="default" size="100%">Oxford, Kristie</style></author><author><style face="normal" font="default" size="100%">Teuton, Jeremy</style></author><author><style face="normal" font="default" size="100%">Kocakaya, Ebru</style></author><author><style face="normal" font="default" size="100%">Summak, Gökçe Yağmur</style></author><author><style face="normal" font="default" size="100%">Hanspers, Kristina</style></author><author><style face="normal" font="default" size="100%">Kutmon, Martina</style></author><author><style face="normal" font="default" size="100%">Coort, Susan</style></author><author><style face="normal" font="default" size="100%">Eijssen, Lars</style></author><author><style face="normal" font="default" size="100%">Ehrhart, Friederike</style></author><author><style face="normal" font="default" size="100%">Rex, Devasahayam Arokia Balaya</style></author><author><style face="normal" font="default" size="100%">Slenter, Denise</style></author><author><style face="normal" font="default" size="100%">Martens, Marvin</style></author><author><style face="normal" font="default" size="100%">Pham, Nhung</style></author><author><style face="normal" font="default" size="100%">Haw, Robin</style></author><author><style face="normal" font="default" size="100%">Jassal, Bijay</style></author><author><style face="normal" font="default" size="100%">Matthews, Lisa</style></author><author><style face="normal" font="default" size="100%">Orlic-Milacic, Marija</style></author><author><style face="normal" font="default" size="100%">Senff Ribeiro, Andrea</style></author><author><style face="normal" font="default" size="100%">Rothfels, Karen</style></author><author><style face="normal" font="default" size="100%">Shamovsky, Veronica</style></author><author><style face="normal" font="default" size="100%">Stephan, Ralf</style></author><author><style face="normal" font="default" size="100%">Sevilla, Cristoffer</style></author><author><style face="normal" font="default" size="100%">Varusai, Thawfeek</style></author><author><style face="normal" font="default" size="100%">Ravel, Jean-Marie</style></author><author><style face="normal" font="default" size="100%">Fraser, Rupsha</style></author><author><style face="normal" font="default" size="100%">Ortseifen, Vera</style></author><author><style face="normal" font="default" size="100%">Marchesi, Silvia</style></author><author><style face="normal" font="default" size="100%">Gawron, Piotr</style></author><author><style face="normal" font="default" size="100%">Smula, Ewa</style></author><author><style face="normal" font="default" size="100%">Heirendt, Laurent</style></author><author><style face="normal" font="default" size="100%">Satagopam, Venkata</style></author><author><style face="normal" font="default" size="100%">Wu, Guanming</style></author><author><style face="normal" font="default" size="100%">Riutta, Anders</style></author><author><style face="normal" font="default" size="100%">Golebiewski, Martin</style></author><author><style face="normal" font="default" size="100%">Owen, Stuart</style></author><author><style face="normal" font="default" size="100%">Goble, Carole</style></author><author><style face="normal" font="default" size="100%">Hu, Xiaoming</style></author><author><style face="normal" font="default" size="100%">Overall, Rupert W</style></author><author><style face="normal" font="default" size="100%">Maier, Dieter</style></author><author><style face="normal" font="default" size="100%">Bauch, Angela</style></author><author><style face="normal" font="default" size="100%">Gyori, Benjamin M</style></author><author><style face="normal" font="default" size="100%">Bachman, John A</style></author><author><style face="normal" font="default" size="100%">Vega, Carlos</style></author><author><style face="normal" font="default" size="100%">Grouès, Valentin</style></author><author><style face="normal" font="default" size="100%">Vazquez, Miguel</style></author><author><style face="normal" font="default" size="100%">Porras, Pablo</style></author><author><style face="normal" font="default" size="100%">Licata, Luana</style></author><author><style face="normal" font="default" size="100%">Iannuccelli, Marta</style></author><author><style face="normal" font="default" size="100%">Sacco, Francesca</style></author><author><style face="normal" font="default" size="100%">Nesterova, Anastasia</style></author><author><style face="normal" font="default" size="100%">Yuryev, Anton</style></author><author><style face="normal" font="default" size="100%">de Waard, Anita</style></author><author><style face="normal" font="default" size="100%">Turei, Denes</style></author><author><style face="normal" font="default" size="100%">Luna, Augustin</style></author><author><style face="normal" font="default" size="100%">Babur, Ozgun</style></author><author><style face="normal" font="default" size="100%">Soliman, Sylvain</style></author><author><style face="normal" font="default" size="100%">Valdeolivas, Alberto</style></author><author><style face="normal" font="default" size="100%">Esteban-Medina, Marina</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Rian, Kinza</style></author><author><style face="normal" font="default" size="100%">Helikar, Tomáš</style></author><author><style face="normal" font="default" size="100%">Puniya, Bhanwar Lal</style></author><author><style face="normal" font="default" size="100%">Modos, Dezso</style></author><author><style face="normal" font="default" size="100%">Treveil, Agatha</style></author><author><style face="normal" font="default" size="100%">Olbei, Marton</style></author><author><style face="normal" font="default" size="100%">De Meulder, Bertrand</style></author><author><style face="normal" font="default" size="100%">Ballereau, Stephane</style></author><author><style face="normal" font="default" size="100%">Dugourd, Aurélien</style></author><author><style face="normal" font="default" size="100%">Naldi, Aurélien</style></author><author><style face="normal" font="default" size="100%">Noël, Vincent</style></author><author><style face="normal" font="default" size="100%">Calzone, Laurence</style></author><author><style face="normal" font="default" size="100%">Sander, Chris</style></author><author><style face="normal" font="default" size="100%">Demir, Emek</style></author><author><style face="normal" font="default" size="100%">Korcsmaros, Tamas</style></author><author><style face="normal" font="default" size="100%">Freeman, Tom C</style></author><author><style face="normal" font="default" size="100%">Augé, Franck</style></author><author><style face="normal" font="default" size="100%">Beckmann, Jacques S</style></author><author><style face="normal" font="default" size="100%">Hasenauer, Jan</style></author><author><style face="normal" font="default" size="100%">Wolkenhauer, Olaf</style></author><author><style face="normal" font="default" size="100%">Wilighagen, Egon L</style></author><author><style face="normal" font="default" size="100%">Pico, Alexander R</style></author><author><style face="normal" font="default" size="100%">Evelo, Chris T</style></author><author><style face="normal" font="default" size="100%">Gillespie, Marc E</style></author><author><style face="normal" font="default" size="100%">Stein, Lincoln D</style></author><author><style face="normal" font="default" size="100%">Hermjakob, Henning</style></author><author><style face="normal" font="default" size="100%">D'Eustachio, Peter</style></author><author><style face="normal" font="default" size="100%">Saez-Rodriguez, Julio</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Valencia, Alfonso</style></author><author><style face="normal" font="default" size="100%">Kitano, Hiroaki</style></author><author><style face="normal" font="default" size="100%">Barillot, Emmanuel</style></author><author><style face="normal" font="default" size="100%">Auffray, Charles</style></author><author><style face="normal" font="default" size="100%">Balling, Rudi</style></author><author><style face="normal" font="default" size="100%">Schneider, Reinhard</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">COVID-19 Disease Map Community</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Syst Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol Syst Biol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Antiviral Agents</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Graphics</style></keyword><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">Cytokines</style></keyword><keyword><style  face="normal" font="default" size="100%">Data Mining</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Factual</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Host Microbial Interactions</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Immunity, Cellular</style></keyword><keyword><style  face="normal" font="default" size="100%">Immunity, Humoral</style></keyword><keyword><style  face="normal" font="default" size="100%">Immunity, Innate</style></keyword><keyword><style  face="normal" font="default" size="100%">Lymphocytes</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolic Networks and Pathways</style></keyword><keyword><style  face="normal" font="default" size="100%">Myeloid Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Interaction Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">SARS-CoV-2</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Viral Proteins</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 10</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">e10387</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/34664389?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Roldán, Gema</style></author><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">Ortuno, Francisco M</style></author><author><style face="normal" font="default" size="100%">Carmona, Rosario</style></author><author><style face="normal" font="default" size="100%">Aquino, Virginia</style></author><author><style face="normal" font="default" size="100%">López-López, Daniel</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Fernandez-Rueda, Jose L</style></author><author><style face="normal" font="default" size="100%">Gallego, Asunción</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">González-Neira, Anna</style></author><author><style face="normal" font="default" size="100%">Pita, Guillermo</style></author><author><style face="normal" font="default" size="100%">Núñez-Torres, Rocío</style></author><author><style face="normal" font="default" size="100%">Santoyo-López, Javier</style></author><author><style face="normal" font="default" size="100%">Ayuso, Carmen</style></author><author><style face="normal" font="default" size="100%">Minguez, Pablo</style></author><author><style face="normal" font="default" size="100%">Avila-Fernandez, Almudena</style></author><author><style face="normal" font="default" size="100%">Corton, Marta</style></author><author><style face="normal" font="default" size="100%">Moreno-Pelayo, Miguel Ángel</style></author><author><style face="normal" font="default" size="100%">Morin, Matías</style></author><author><style face="normal" font="default" size="100%">Gallego-Martinez, Alvaro</style></author><author><style face="normal" font="default" size="100%">Lopez-Escamez, Jose A</style></author><author><style face="normal" font="default" size="100%">Borrego, Salud</style></author><author><style face="normal" font="default" size="100%">Antiňolo, Guillermo</style></author><author><style face="normal" font="default" size="100%">Amigo, Jorge</style></author><author><style face="normal" font="default" size="100%">Salgado-Garrido, Josefa</style></author><author><style face="normal" font="default" size="100%">Pasalodos-Sanchez, Sara</style></author><author><style face="normal" font="default" size="100%">Morte, Beatriz</style></author><author><style face="normal" font="default" size="100%">Carracedo, Ángel</style></author><author><style face="normal" font="default" size="100%">Alonso, Ángel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">Spanish Exome Crowdsourcing Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">CSVS, a crowdsourcing database of the Spanish population genetic variability.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Alleles</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromosome Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Crowdsourcing</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Frequency</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetics, Population</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Precision Medicine</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Spain</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 01 08</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">49</style></volume><pages><style face="normal" font="default" size="100%">D1130-D1137</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The knowledge of the genetic variability of the local population is of utmost importance in personalized medicine and has been revealed as a critical factor for the discovery of new disease variants. Here, we present the Collaborative Spanish Variability Server (CSVS), which currently contains more than 2000 genomes and exomes of unrelated Spanish individuals. This database has been generated in a collaborative crowdsourcing effort collecting sequencing data produced by local genomic projects and for other purposes. Sequences have been grouped by ICD10 upper categories. A web interface allows querying the database removing one or more ICD10 categories. In this way, aggregated counts of allele frequencies of the pseudo-control Spanish population can be obtained for diseases belonging to the category removed. Interestingly, in addition to pseudo-control studies, some population studies can be made, as, for example, prevalence of pharmacogenomic variants, etc. In addition, this genomic data has been used to define the first Spanish Genome Reference Panel (SGRP1.0) for imputation. This is the first local repository of variability entirely produced by a crowdsourcing effort and constitutes an example for future initiatives to characterize local variability worldwide. CSVS is also part of the GA4GH Beacon network. CSVS can be accessed at: http://csvs.babelomics.org/.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">D1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32990755?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ostaszewski, Marek</style></author><author><style face="normal" font="default" size="100%">Mazein, Alexander</style></author><author><style face="normal" font="default" size="100%">Gillespie, Marc E</style></author><author><style face="normal" font="default" size="100%">Kuperstein, Inna</style></author><author><style face="normal" font="default" size="100%">Niarakis, Anna</style></author><author><style face="normal" font="default" size="100%">Hermjakob, Henning</style></author><author><style face="normal" font="default" size="100%">Pico, Alexander R</style></author><author><style face="normal" font="default" size="100%">Willighagen, Egon L</style></author><author><style face="normal" font="default" size="100%">Evelo, Chris T</style></author><author><style face="normal" font="default" size="100%">Hasenauer, Jan</style></author><author><style face="normal" font="default" size="100%">Schreiber, Falk</style></author><author><style face="normal" font="default" size="100%">Dräger, Andreas</style></author><author><style face="normal" font="default" size="100%">Demir, Emek</style></author><author><style face="normal" font="default" size="100%">Wolkenhauer, Olaf</style></author><author><style face="normal" font="default" size="100%">Furlong, Laura I</style></author><author><style face="normal" font="default" size="100%">Barillot, Emmanuel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Orta-Resendiz, Aurelio</style></author><author><style face="normal" font="default" size="100%">Messina, Francesco</style></author><author><style face="normal" font="default" size="100%">Valencia, Alfonso</style></author><author><style face="normal" font="default" size="100%">Funahashi, Akira</style></author><author><style face="normal" font="default" size="100%">Kitano, Hiroaki</style></author><author><style face="normal" font="default" size="100%">Auffray, Charles</style></author><author><style face="normal" font="default" size="100%">Balling, Rudi</style></author><author><style face="normal" font="default" size="100%">Schneider, Reinhard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Data</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci Data</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Betacoronavirus</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Coronavirus Infections</style></keyword><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Factual</style></keyword><keyword><style  face="normal" font="default" size="100%">Host Microbial Interactions</style></keyword><keyword><style  face="normal" font="default" size="100%">Host-Pathogen Interactions</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">International Cooperation</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Biological</style></keyword><keyword><style  face="normal" font="default" size="100%">Pandemics</style></keyword><keyword><style  face="normal" font="default" size="100%">Pneumonia, Viral</style></keyword><keyword><style  face="normal" font="default" size="100%">SARS-CoV-2</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 05 05</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">136</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32371892?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">Hidalgo, Marta R</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A comparison of mechanistic signaling pathway activity analysis methods.</style></title><secondary-title><style face="normal" font="default" size="100%">Brief Bioinform</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Brief Bioinform</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Postmortem Changes</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Sep 27</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">1655-1668</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/29868818?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sanghez, Valentina</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Sebastián-Leon, Patricia</style></author><author><style face="normal" font="default" size="100%">Carobbio, Stefania</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Vidal-Puig, Antonio</style></author><author><style face="normal" font="default" size="100%">Bartolomucci, Alessandro</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Chronic subordination stress selectively downregulates the insulin signaling pathway in liver and skeletal muscle but not in adipose tissue of male mice.</style></title><secondary-title><style face="normal" font="default" size="100%">Stress (Amsterdam, Netherlands)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adipose tissue</style></keyword><keyword><style  face="normal" font="default" size="100%">insulin</style></keyword><keyword><style  face="normal" font="default" size="100%">IRS1</style></keyword><keyword><style  face="normal" font="default" size="100%">IRS2</style></keyword><keyword><style  face="normal" font="default" size="100%">metabolic syndrome</style></keyword><keyword><style  face="normal" font="default" size="100%">obesity</style></keyword><keyword><style  face="normal" font="default" size="100%">pathway analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 Mar 7</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.tandfonline.com/doi/abs/10.3109/10253890.2016.1151491?journalCode=ists20</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">1-11</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Chronic stress has been associated with obesity, glucose intolerance, and insulin resistance. We developed a model of chronic psychosocial stress (CPS) in which subordinate mice are vulnerable to obesity and the metabolic-like syndrome while dominant mice exhibit a healthy metabolic phenotype. Here we tested the hypothesis that the metabolic difference between subordinate and dominant mice is associated with changes in functional pathways relevant for insulin sensitivity, glucose and lipid homeostasis. Male mice were exposed to CPS for four weeks and fed either a standard diet or a high-fat diet (HFD). We first measured, by real-time PCR candidate genes, in the liver, skeletal muscle, and the perigonadal white adipose tissue (pWAT). Subsequently, we used a probabilistic analysis approach to analyze different ways in which signals can be transmitted across the pathways in each tissue. Results showed that subordinate mice displayed a drastic downregulation of the insulin pathway in liver and muscle, indicative of insulin resistance, already on standard diet. Conversely, pWAT showed molecular changes suggestive of facilitated fat deposition in an otherwise insulin-sensitive tissue. The molecular changes in subordinate mice fed a standard diet were greater compared to HFD-fed controls. Finally, dominant mice maintained a substantially normal metabolic and molecular phenotype even when fed a HFD. Overall, our data demonstrate that subordination stress is a potent stimulus for the downregulation of the insulin signaling pathway in liver and muscle and a major risk factor for the development of obesity, insulin resistance, and type 2 diabetes mellitus.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martinez, Hector</style></author><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Barrachina, Sergio</style></author><author><style face="normal" font="default" size="100%">Castillo, Maribel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Quintana-Orti, Enrique S</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Concurrent and Accurate Short Read Mapping on Multicore Processors.</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">HPC</style></keyword><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword><keyword><style  face="normal" font="default" size="100%">short real mapping</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015 Sep-Oct</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&amp;arnumber=7010005</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">995-1007</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We introduce a parallel aligner with a work-flow organization for fast and accurate mapping of RNA sequences on servers equipped with multicore processors. Our software, [Formula: see text] ([Formula: see text] is an open-source application. The software is available at http://www.opencb.org, exploits a suffix array to rapidly map a large fraction of the RNA fragments (reads), as well as leverages the accuracy of the Smith-Waterman algorithm to deal with conflictive reads. The aligner is enhanced with a careful strategy to detect splice junctions based on an adaptive division of RNA reads into small segments (or seeds), which are then mapped onto a number of candidate alignment locations, providing crucial information for the successful alignment of the complete reads. The experimental results on a platform with Intel multicore technology report the parallel performance of [Formula: see text], on RNA reads of 100-400 nucleotides, which excels in execution time/sensitivity to state-of-the-art aligners such as TopHat 2+Bowtie 2, MapSplice, and STAR.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carmona, F Javier</style></author><author><style face="normal" font="default" size="100%">Davalos, Veronica</style></author><author><style face="normal" font="default" size="100%">Vidal, Enrique</style></author><author><style face="normal" font="default" size="100%">Gomez, Antonio</style></author><author><style face="normal" font="default" size="100%">Heyn, Holger</style></author><author><style face="normal" font="default" size="100%">Hashimoto, Yutaka</style></author><author><style face="normal" font="default" size="100%">Vizoso, Miguel</style></author><author><style face="normal" font="default" size="100%">Martinez-Cardus, Anna</style></author><author><style face="normal" font="default" size="100%">Sayols, Sergi</style></author><author><style face="normal" font="default" size="100%">Ferreira, Humberto</style></author><author><style face="normal" font="default" size="100%">Sanchez-Mut, Jose</style></author><author><style face="normal" font="default" size="100%">Moran, Sebastian</style></author><author><style face="normal" font="default" size="100%">Margeli, Mireia</style></author><author><style face="normal" font="default" size="100%">Castella, Eva</style></author><author><style face="normal" font="default" size="100%">Berdasco, Maria</style></author><author><style face="normal" font="default" size="100%">Stefansson, Olafur Andri</style></author><author><style face="normal" font="default" size="100%">Eyfjord, Jorunn E</style></author><author><style face="normal" font="default" size="100%">Gonzalez-Suarez, Eva</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Orozco, Modesto</style></author><author><style face="normal" font="default" size="100%">Gut, Ivo</style></author><author><style face="normal" font="default" size="100%">Esteller, Manel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Comprehensive DNA Methylation Profile of Epithelial-to-Mesenchymal Transition.</style></title><secondary-title><style face="normal" font="default" size="100%">Cancer research</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Methyl-Seq</style></keyword><keyword><style  face="normal" font="default" size="100%">Methylomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Next Generation Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014 Aug 8</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/25106427</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">19</style></number><volume><style face="normal" font="default" size="100%">74</style></volume><pages><style face="normal" font="default" size="100%">5608–19</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Epithelial-to-mesenchymal transition (EMT) is a plastic process in which fully differentiated epithelial cells are converted into poorly differentiated, migratory and invasive mesenchymal cells and it has been related to the metastasis potential of tumors. This is a reversible process and cells can also eventually undergo mesenchymal-to-epithelial transition (MET). The existence of a dynamic EMT process suggests the involvement of epigenetic shifts in the phenotype. Herein, we obtained the DNA methylomes at single-base resolution of MDCK cells undergoing epithelial-to-mesenchymal transition (EMT) and translated the identified differentially methylated regions (DMRs) to human breast cancer cells undergoing a gain of migratory and invasive capabilities associated with the EMT phenotype. We noticed dynamic and reversible changes of DNA methylation, both on promoter sequences and gene-bodies in association with transcription regulation of EMT-related genes. Most importantly, the identified DNA methylation markers of EMT were present in primary mammary tumors in association with the epithelial or the mesenchymal phenotype of the studied breast cancer samples.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bleda, Marta</style></author><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">De Maria, Alejandro</style></author><author><style face="normal" font="default" size="100%">Salavert, Francisco</style></author><author><style face="normal" font="default" size="100%">García-Alonso, Luz</style></author><author><style face="normal" font="default" size="100%">Celma, Matilde</style></author><author><style face="normal" font="default" size="100%">Martin, Ainoha</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CellBase, a comprehensive collection of RESTful web services for retrieving relevant biological information from heterogeneous sources.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic acids research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012 Jul</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://nar.oxfordjournals.org/content/40/W1/W609.long</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">40</style></volume><pages><style face="normal" font="default" size="100%">W609-14</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">During the past years, the advances in high-throughput technologies have produced an unprecedented growth in the number and size of repositories and databases storing relevant biological data. Today, there is more biological information than ever but, unfortunately, the current status of many of these repositories is far from being optimal. Some of the most common problems are that the information is spread out in many small databases; frequently there are different standards among repositories and some databases are no longer supported or they contain too specific and unconnected information. In addition, data size is increasingly becoming an obstacle when accessing or storing biological data. All these issues make very difficult to extract and integrate information from different sources, to analyze experiments or to access and query this information in a programmatic way. CellBase provides a solution to the growing necessity of integration by easing the access to biological data. CellBase implements a set of RESTful web services that query a centralized database containing the most relevant biological data sources. The database is hosted in our servers and is regularly updated. CellBase documentation can be found at http://docs.bioinfo.cipf.es/projects/cellbase.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Javierre, Biola M</style></author><author><style face="normal" font="default" size="100%">Fernandez, Agustin F</style></author><author><style face="normal" font="default" size="100%">Richter, Julia</style></author><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Martin-Subero, J Ignacio</style></author><author><style face="normal" font="default" size="100%">Rodriguez-Ubreva, Javier</style></author><author><style face="normal" font="default" size="100%">Berdasco, Maria</style></author><author><style face="normal" font="default" size="100%">Fraga, Mario F</style></author><author><style face="normal" font="default" size="100%">O’Hanlon, Terrance P</style></author><author><style face="normal" font="default" size="100%">Rider, Lisa G</style></author><author><style face="normal" font="default" size="100%">Jacinto, Filipe V</style></author><author><style face="normal" font="default" size="100%">Lopez-Longo, F Javier</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Forn, Marta</style></author><author><style face="normal" font="default" size="100%">Peinado, Miguel A</style></author><author><style face="normal" font="default" size="100%">Carreño, Luis</style></author><author><style face="normal" font="default" size="100%">Sawalha, Amr H</style></author><author><style face="normal" font="default" size="100%">Harley, John B</style></author><author><style face="normal" font="default" size="100%">Siebert, Reiner</style></author><author><style face="normal" font="default" size="100%">Esteller, Manel</style></author><author><style face="normal" font="default" size="100%">Miller, Frederick W</style></author><author><style face="normal" font="default" size="100%">Ballestar, Esteban</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Changes in the pattern of DNA methylation associate with twin discordance in systemic lupus erythematosus.</style></title><secondary-title><style face="normal" font="default" size="100%">Genome research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010 Feb</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">170-9</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Monozygotic (MZ) twins are partially concordant for most complex diseases, including autoimmune disorders. Whereas phenotypic concordance can be used to study heritability, discordance suggests the role of non-genetic factors. In autoimmune diseases, environmentally driven epigenetic changes are thought to contribute to their etiology. Here we report the first high-throughput and candidate sequence analyses of DNA methylation to investigate discordance for autoimmune disease in twins. We used a cohort of MZ twins discordant for three diseases whose clinical signs often overlap: systemic lupus erythematosus (SLE), rheumatoid arthritis, and dermatomyositis. Only MZ twins discordant for SLE featured widespread changes in the DNA methylation status of a significant number of genes. Gene ontology analysis revealed enrichment in categories associated with immune function. Individual analysis confirmed the existence of DNA methylation and expression changes in genes relevant to SLE pathogenesis. These changes occurred in parallel with a global decrease in the 5-methylcytosine content that was concomitantly accompanied with changes in DNA methylation and expression levels of ribosomal RNA genes, although no changes in repetitive sequences were found. Our findings not only identify potentially relevant DNA methylation markers for the clinical characterization of SLE patients but also support the notion that epigenetic changes may be critical in the clinical manifestations of autoimmune disease.&lt;/p&gt;</style></abstract></record></records></xml>