<?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%">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%">Garrido-Rodriguez, Martín</style></author><author><style face="normal" font="default" size="100%">López-López, Daniel</style></author><author><style face="normal" font="default" size="100%">Ortuno, Francisco M</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Muñoz, Eduardo</style></author><author><style face="normal" font="default" size="100%">Calzado, Marco A</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 versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS Comput Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS Comput Biol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Line, Tumor</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%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Theoretical</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA-seq</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%">Transcriptome</style></keyword><keyword><style  face="normal" font="default" size="100%">whole exome sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Workflow</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 02</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">e1008748</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33571195?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%">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%">Loucera, Carlos</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%">Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models.</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%">Databases, Factual</style></keyword><keyword><style  face="normal" font="default" size="100%">Fanconi Anemia</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%">Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</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 Jul 02</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">370</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;In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;The application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;The use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.&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/31266445?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%">Rodríguez Hernáez, Javier</style></author><author><style face="normal" font="default" size="100%">Cerón Cucchi, Maria Esperanza</style></author><author><style face="normal" font="default" size="100%">Cravero, Silvio</style></author><author><style face="normal" font="default" size="100%">Martinez, Maria Carolina</style></author><author><style face="normal" font="default" size="100%">Gonzalez, Sergio</style></author><author><style face="normal" font="default" size="100%">Puebla, Andrea</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Farber, Marisa</style></author><author><style face="normal" font="default" size="100%">Paniego, Norma</style></author><author><style face="normal" font="default" size="100%">Rivarola, Máximo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The first complete genomic structure of Butyrivibrio fibrisolvens and its chromid.</style></title><secondary-title><style face="normal" font="default" size="100%">Microb Genom</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Microb Genom</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Butyrivibrio fibrisolvens</style></keyword><keyword><style  face="normal" font="default" size="100%">Cattle</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Bacterial</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%">Milk</style></keyword><keyword><style  face="normal" font="default" size="100%">Rumen</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 10</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">4</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Butyrivibrio fibrisolvens forms part of the gastrointestinal microbiome of ruminants and other mammals, including humans. Indeed, it is one of the most common bacteria found in the rumen and plays an important role in ruminal fermentation of polysaccharides, yet, to date, there is no closed reference genome published for this species in any ruminant animal. We successfully assembled the nearly complete genome sequence of B. fibrisolvens strain INBov1 isolated from cow rumen using Illumina paired-end reads, 454 Roche single-end and mate pair sequencing technology. Additionally, we constructed an optical restriction map of this strain to aid in scaffold ordering and positioning, and completed the first genomic structure of this species. Moreover, we identified and assembled the first chromid of this species (pINBov266). The INBov1 genome encodes a large set of genes involved in the cellulolytic process but lacks key genes. This seems to indicate that B. fibrisolvens plays an important role in ruminal cellulolytic processes, but does not have autonomous cellulolytic capacity. When searching for genes involved in the biohydrogenation of unsaturated fatty acids, no linoleate isomerase gene was found in this strain. INBov1 does encode oleate hydratase genes known to participate in the hydrogenation of oleic acids. Furthermore, INBov1 contains an enolase gene, which has been recently determined to participate in the synthesis of conjugated linoleic acids. This work confirms the presence of a novel chromid in B. fibrisolvens and provides a new potential reference genome sequence for this species, providing new insight into its role in biohydrogenation and carbohydrate degradation.&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/30216146?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%">Wu, Guohong Albert</style></author><author><style face="normal" font="default" size="100%">Terol, Javier</style></author><author><style face="normal" font="default" size="100%">Ibañez, Victoria</style></author><author><style face="normal" font="default" size="100%">López-García, Antonio</style></author><author><style face="normal" font="default" size="100%">Pérez-Román, Estela</style></author><author><style face="normal" font="default" size="100%">Borredá, Carles</style></author><author><style face="normal" font="default" size="100%">Domingo, Concha</style></author><author><style face="normal" font="default" size="100%">Tadeo, Francisco R</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Alonso, Roberto</style></author><author><style face="normal" font="default" size="100%">Curk, Franck</style></author><author><style face="normal" font="default" size="100%">Du, Dongliang</style></author><author><style face="normal" font="default" size="100%">Ollitrault, Patrick</style></author><author><style face="normal" font="default" size="100%">Roose, Mikeal L</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Gmitter, Frederick G</style></author><author><style face="normal" font="default" size="100%">Rokhsar, Daniel S</style></author><author><style face="normal" font="default" size="100%">Talon, Manuel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genomics of the origin and evolution of Citrus.</style></title><secondary-title><style face="normal" font="default" size="100%">Nature</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nature</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Asia, Southeastern</style></keyword><keyword><style  face="normal" font="default" size="100%">Biodiversity</style></keyword><keyword><style  face="normal" font="default" size="100%">citrus</style></keyword><keyword><style  face="normal" font="default" size="100%">Crop Production</style></keyword><keyword><style  face="normal" font="default" size="100%">Evolution, Molecular</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Speciation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Haplotypes</style></keyword><keyword><style  face="normal" font="default" size="100%">Heterozygote</style></keyword><keyword><style  face="normal" font="default" size="100%">History, Ancient</style></keyword><keyword><style  face="normal" font="default" size="100%">Human Migration</style></keyword><keyword><style  face="normal" font="default" size="100%">Hybridization, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Phylogeny</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 02 15</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">554</style></volume><pages><style face="normal" font="default" size="100%">311-316</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 genus Citrus, comprising some of the most widely cultivated fruit crops worldwide, includes an uncertain number of species. Here we describe ten natural citrus species, using genomic, phylogenetic and biogeographic analyses of 60 accessions representing diverse citrus germ plasms, and propose that citrus diversified during the late Miocene epoch through a rapid southeast Asian radiation that correlates with a marked weakening of the monsoons. A second radiation enabled by migration across the Wallace line gave rise to the Australian limes in the early Pliocene epoch. Further identification and analyses of hybrids and admixed genomes provides insights into the genealogy of major commercial cultivars of citrus. Among mandarins and sweet orange, we find an extensive network of relatedness that illuminates the domestication of these groups. Widespread pummelo admixture among these mandarins and its correlation with fruit size and acidity suggests a plausible role of pummelo introgression in the selection of palatable mandarins. This work provides a new evolutionary framework for the genus Citrus.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7692</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/29414943?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%">Puig-Butille, Joan Anton</style></author><author><style face="normal" font="default" size="100%">Gimenez-Xavier, Pol</style></author><author><style face="normal" font="default" size="100%">Visconti, Alessia</style></author><author><style face="normal" font="default" size="100%">Nsengimana, Jérémie</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Tell-Marti, Gemma</style></author><author><style face="normal" font="default" size="100%">Escamez, Maria José</style></author><author><style face="normal" font="default" size="100%">Newton-Bishop, Julia</style></author><author><style face="normal" font="default" size="100%">Bataille, Veronique</style></author><author><style face="normal" font="default" size="100%">Del Rio, Marcela</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Falchi, Mario</style></author><author><style face="normal" font="default" size="100%">Puig, Susana</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genomic expression differences between cutaneous cells from red hair color individuals and black hair color individuals based on bioinformatic analysis.</style></title><secondary-title><style face="normal" font="default" size="100%">Oncotarget</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Oncotarget</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Coculture Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">gene expression</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Hair Color</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Keratinocytes</style></keyword><keyword><style  face="normal" font="default" size="100%">Melanocytes</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Receptor, Melanocortin, Type 1</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Feb 14</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.impactjournals.com/oncotarget/index.php?journal=oncotarget&amp;page=article&amp;op=view&amp;path%5B%5D=14140&amp;path%5B%5D=45094</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">11589-11599</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 MC1R gene plays a crucial role in pigmentation synthesis. Loss-of-function MC1R variants, which impair protein function, are associated with red hair color (RHC) phenotype and increased skin cancer risk. Cultured cutaneous cells bearing loss-of-function MC1R variants show a distinct gene expression profile compared to wild-type MC1R cultured cutaneous cells. We analysed the gene signature associated with RHC co-cultured melanocytes and keratinocytes by Protein-Protein interaction (PPI) network analysis to identify genes related with non-functional MC1R variants. From two detected networks, we selected 23 nodes as hub genes based on topological parameters. Differential expression of hub genes was then evaluated in healthy skin biopsies from RHC and black hair color (BHC) individuals. We also compared gene expression in melanoma tumors from individuals with RHC versus BHC. Gene expression in normal skin from RHC cutaneous cells showed dysregulation in 8 out of 23 hub genes (CLN3, ATG10, WIPI2, SNX2, GABARAPL2, YWHA, PCNA and GBAS). Hub genes did not differ between melanoma tumors in RHC versus BHC individuals. The study suggests that healthy skin cells from RHC individuals present a constitutive genomic deregulation associated with the red hair phenotype and identify novel genes involved in melanocyte biology.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/28030792?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%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">Alonso, Roberto</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%">Conesa, David</style></author><author><style face="normal" font="default" size="100%">López-Quílez, Antonio</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%">Reference genome assessment from a population scale perspective: an accurate profile of variability and noise.</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Bioinformatics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Genotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Statistical</style></keyword><keyword><style  face="normal" font="default" size="100%">Quality Control</style></keyword><keyword><style  face="normal" font="default" size="100%">Reproducibility of Results</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Nov 15</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btx482</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">3511-3517</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;Motivation: &lt;/b&gt;Current plant and animal genomic studies are often based on newly assembled genomes that have not been properly consolidated. In this scenario, misassembled regions can easily lead to false-positive findings. Despite quality control scores are included within genotyping protocols, they are usually employed to evaluate individual sample quality rather than reference sequence reliability. We propose a statistical model that combines quality control scores across samples in order to detect incongruent patterns at every genomic region. Our model is inherently robust since common artifact signals are expected to be shared between independent samples over misassembled regions of the genome.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Results: &lt;/b&gt;The reliability of our protocol has been extensively tested through different experiments and organisms with accurate results, improving state-of-the-art methods. Our analysis demonstrates synergistic relations between quality control scores and allelic variability estimators, that improve the detection of misassembled regions, and is able to find strong artifact signals even within the human reference assembly. Furthermore, we demonstrated how our model can be trained to properly rank the confidence of a set of candidate variants obtained from new independent samples.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Availability and implementation: &lt;/b&gt;This tool is freely available at http://gitlab.com/carbonell/ces.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Contact: &lt;/b&gt;jcarbonell.cipf@gmail.com or joaquin.dopazo@juntadeandalucia.es.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Supplementary information: &lt;/b&gt;Supplementary data are available at Bioinformatics online.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">22</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/28961772?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%">Salavert, José</style></author><author><style face="normal" font="default" size="100%">Tomás, Andrés</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%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Blanquer, Ignacio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fast inexact mapping using advanced tree exploration on backward search methods.</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%">Algorithms</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%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Alignment</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</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 Jan 28</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">18</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;Short sequence mapping methods for Next Generation Sequencing consist on a combination of seeding techniques followed by local alignment based on dynamic programming approaches. Most seeding algorithms are based on backward search alignment, using the Burrows Wheeler Transform, the Ferragina and Manzini Index or Suffix Arrays. All these backward search algorithms have excellent performance, but their computational cost highly increases when allowing errors. In this paper, we discuss an inexact mapping algorithm based on pruning strategies for search tree exploration over genomic data.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;The proposed algorithm achieves a 13x speed-up over similar algorithms when allowing 6 base errors, including insertions, deletions and mismatches. This algorithm can deal with 400 bps reads with up to 9 errors in a high quality Illumina dataset. In this example, the algorithm works as a preprocessor that reduces by 55% the number of reads to be aligned. Depending on the aligner the overall execution time is reduced between 20-40%.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Although not intended as a complete sequence mapping tool, the proposed algorithm could be used as a preprocessing step to modern sequence mappers. This step significantly reduces the number reads to be aligned, accelerating overall alignment time. Furthermore, this algorithm could be used for accelerating the seeding step of already available sequence mappers. In addition, an out-of-core index has been implemented for working with large genomes on systems without expensive memory configurations.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/25626517?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%">García-Alonso, Luz</style></author><author><style face="normal" font="default" size="100%">Jiménez-Almazán, Jorge</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Vela-Boza, Alicia</style></author><author><style face="normal" font="default" size="100%">Santoyo-López, Javier</style></author><author><style face="normal" font="default" size="100%">Antiňolo, Guillermo</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%">The role of the interactome in the maintenance of deleterious variability in human populations.</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%">Alleles</style></keyword><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Library</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%">Models, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Conformation</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Interaction Maps</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Whites</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 Sep 26</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">752</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Recent genomic projects have revealed the existence of an unexpectedly large amount of deleterious variability in the human genome. Several hypotheses have been proposed to explain such an apparently high mutational load. However, the mechanisms by which deleterious mutations in some genes cause a pathological effect but are apparently innocuous in other genes remain largely unknown. This study searched for deleterious variants in the 1,000 genomes populations, as well as in a newly sequenced population of 252 healthy Spanish individuals. In addition, variants causative of monogenic diseases and somatic variants from 41 chronic lymphocytic leukaemia patients were analysed. The deleterious variants found were analysed in the context of the interactome to understand the role of network topology in the maintenance of the observed mutational load. Our results suggest that one of the mechanisms whereby the effect of these deleterious variants on the phenotype is suppressed could be related to the configuration of the protein interaction network. Most of the deleterious variants observed in healthy individuals are concentrated in peripheral regions of the interactome, in combinations that preserve their connectivity, and have a marginal effect on interactome integrity. On the contrary, likely pathogenic cancer somatic deleterious variants tend to occur in internal regions of the interactome, often with associated structural consequences. Finally, variants causative of monogenic diseases seem to occupy an intermediate position. Our observations suggest that the real pathological potential of a variant might be more a systems property rather than an intrinsic property of individual proteins. &lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/25261458?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%">García-Alonso, Luz</style></author><author><style face="normal" font="default" size="100%">Alonso, Roberto</style></author><author><style face="normal" font="default" size="100%">Vidal, Enrique</style></author><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">De Maria, Alejandro</style></author><author><style face="normal" font="default" size="100%">Minguez, Pablo</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</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%">Discovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments.</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%">Bipolar Disorder</style></keyword><keyword><style  face="normal" font="default" size="100%">Fanconi Anemia</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes, Neoplasm</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</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%">Protein Interaction Mapping</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012 Nov 01</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">40</style></volume><pages><style face="normal" font="default" size="100%">e158</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein-protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">20</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/22844098?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%">Jimenez, Rafael C</style></author><author><style face="normal" font="default" size="100%">Salazar, Gustavo A</style></author><author><style face="normal" font="default" size="100%">Gel, Bernat</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Mulder, Nicola</style></author><author><style face="normal" font="default" size="100%">Corpas, Manuel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">myKaryoView: a light-weight client for visualization of genomic data.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS One</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS One</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computer Graphics</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Sequence Annotation</style></keyword><keyword><style  face="normal" font="default" size="100%">User-Computer Interface</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">e26345</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 Distributed Annotation System (DAS) is a protocol for easy sharing and integration of biological annotations. In order to visualize feature annotations in a genomic context a client is required. Here we present myKaryoView, a simple light-weight DAS tool for visualization of genomic annotation. myKaryoView has been specifically configured to help analyse data derived from personal genomics, although it can also be used as a generic genome browser visualization. Several well-known data sources are provided to facilitate comparison of known genes and normal variation regions. The navigation experience is enhanced by simultaneous rendering of different levels of detail across chromosomes. A simple interface is provided to allow searches for any SNP, gene or chromosomal region. User-defined DAS data sources may also be added when querying the system. We demonstrate myKaryoView capabilities for adding user-defined sources with a set of genetic profiles of family-related individuals downloaded directly from 23andMe. myKaryoView is a web tool for visualization of genomic data specifically designed for direct-to-consumer genomic data that uses publicly available data distributed throughout the Internet. It does not require data to be held locally and it is capable of rendering any feature as long as it conforms to DAS specifications. Configuration and addition of sources to myKaryoView can be done through the interface. Here we show a proof of principle of myKaryoView's ability to display personal genomics data with 23andMe genome data sources. The tool is available at: http://mykaryoview.com.&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/22046276?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%">Serra, François</style></author><author><style face="normal" font="default" size="100%">Arbiza, Leonardo</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Dopazo, Hernán</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Natural selection on functional modules, a genome-wide analysis.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS Comput Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS Comput Biol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Drosophila</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Insect</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Mammals</style></keyword><keyword><style  face="normal" font="default" size="100%">Phylogeny</style></keyword><keyword><style  face="normal" font="default" size="100%">Selection, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Mar</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">e1001093</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Classically, the functional consequences of natural selection over genomes have been analyzed as the compound effects of individual genes. The current paradigm for large-scale analysis of adaptation is based on the observed significant deviations of rates of individual genes from neutral evolutionary expectation. This approach, which assumed independence among genes, has not been able to identify biological functions significantly enriched in positively selected genes in individual species. Alternatively, pooling related species has enhanced the search for signatures of selection. However, grouping signatures does not allow testing for adaptive differences between species. Here we introduce the Gene-Set Selection Analysis (GSSA), a new genome-wide approach to test for evidences of natural selection on functional modules. GSSA is able to detect lineage specific evolutionary rate changes in a notable number of functional modules. For example, in nine mammal and Drosophilae genomes GSSA identifies hundreds of functional modules with significant associations to high and low rates of evolution. Many of the detected functional modules with high evolutionary rates have been previously identified as biological functions under positive selection. Notably, GSSA identifies conserved functional modules with many positively selected genes, which questions whether they are exclusively selected for fitting genomes to environmental changes. Our results agree with previous studies suggesting that adaptation requires positive selection, but not every mutation under positive selection contributes to the adaptive dynamical process of the evolution of species.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/21390268?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%">Sánchez, Rubén</style></author><author><style face="normal" font="default" size="100%">Serra, François</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%">Carbonell, José</style></author><author><style face="normal" font="default" size="100%">Pulido, Luis</style></author><author><style face="normal" font="default" size="100%">De Maria, Alejandro</style></author><author><style face="normal" font="default" size="100%">Capella-Gutíerrez, Salvador</style></author><author><style face="normal" font="default" size="100%">Huerta-Cepas, Jaime</style></author><author><style face="normal" font="default" size="100%">Gabaldón, Toni</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Dopazo, Hernán</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Phylemon 2.0: a suite of web-tools for molecular evolution, phylogenetics, phylogenomics and hypotheses testing.</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%">Evolution, Molecular</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Phylogeny</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Alignment</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Jul</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">39</style></volume><pages><style face="normal" font="default" size="100%">W470-4</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Phylemon 2.0 is a new release of the suite of web tools for molecular evolution, phylogenetics, phylogenomics and hypotheses testing. It has been designed as a response to the increasing demand of molecular sequence analyses for experts and non-expert users. Phylemon 2.0 has several unique features that differentiates it from other similar web resources: (i) it offers an integrated environment that enables evolutionary analyses, format conversion, file storage and edition of results; (ii) it suggests further analyses, thereby guiding the users through the web server; and (iii) it allows users to design and save phylogenetic pipelines to be used over multiple genes (phylogenomics). Altogether, Phylemon 2.0 integrates a suite of 30 tools covering sequence alignment reconstruction and trimming; tree reconstruction, visualization and manipulation; and evolutionary hypotheses testing.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">Web Server issue</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/21646336?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%">Minguez, Pablo</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%">Functional genomics and networks: new approaches in the extraction of complex gene modules.</style></title><secondary-title><style face="normal" font="default" size="100%">Expert Rev Proteomics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Expert Rev Proteomics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Binding</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems biology</style></keyword></keywords><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%">7</style></volume><pages><style face="normal" font="default" size="100%">55-63</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 engine that makes the cell work is made of an intricate network of molecular interactions. Nowadays, the elements and relationships of this complex network can be studied with several types of high-throughput techniques. The dream of having a global picture of the cell from different perspectives that can jointly explain cell behavior is, at least technically, feasible. However, this task can only be accomplished by filling the gap between data and information. The availability of methods capable of accurately managing, integrating and analyzing the results from these experiments is crucial for this purpose. Here, we review the new challenges raised by the availability of different genomic data, as well as the new proposals presented to cope with the increasing data complexity. Special emphasis is given to approaches that explore the transcriptome trying to describe the modules of genes that account for the traits studied.&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/20121476?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%">Galan, Amparo</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Póo, M Eugenia</style></author><author><style face="normal" font="default" size="100%">Valbuena, Diana</style></author><author><style face="normal" font="default" size="100%">Ruiz, Veronica</style></author><author><style face="normal" font="default" size="100%">Aguilar, Cristóbal</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Simon, Carlos</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Functional genomics of 5- to 8-cell stage human embryos by blastomere single-cell cDNA analysis.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS One</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS One</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Blastomeres</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA, Complementary</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</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%">Oligonucleotide Array Sequence Analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010 Oct 26</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">e13615</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Blastomere fate and embryonic genome activation (EGA) during human embryonic development are unsolved areas of high scientific and clinical interest. Forty-nine blastomeres from 5- to 8-cell human embryos have been investigated following an efficient single-cell cDNA amplification protocol to provide a template for high-density microarray analysis. The previously described markers, characteristic of Inner Cell Mass (ICM) (n = 120), stemness (n = 190) and Trophectoderm (TE) (n = 45), were analyzed, and a housekeeping pattern of 46 genes was established. All the human blastomeres from the 5- to 8-cell stage embryo displayed a common gene expression pattern corresponding to ICM markers (e.g., DDX3, FOXD3, LEFTY1, MYC, NANOG, POU5F1), stemness (e.g., POU5F1, DNMT3B, GABRB3, SOX2, ZFP42, TERT), and TE markers (e.g., GATA6, EOMES, CDX2, LHCGR). The EGA profile was also investigated between the 5-6- and 8-cell stage embryos, and compared to the blastocyst stage. Known genes (n = 92) such as depleted maternal transcripts (e.g., CCNA1, CCNB1, DPPA2) and embryo-specific activation (e.g., POU5F1, CDH1, DPPA4), as well as novel genes, were confirmed. In summary, the global single-cell cDNA amplification microarray analysis of the 5- to 8-cell stage human embryos reveals that blastomere fate is not committed to ICM or TE. Finally, new EGA features in human embryogenesis are presented.&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/21049019?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%">Montaner, David</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%">Multidimensional gene set analysis of genomic data.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS One</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS One</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</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%">Models, Statistical</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010 Apr 27</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">e10348</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 functional implications of changes in gene expression, mutations, etc., is the aim of most genomic experiments. To achieve this, several functional profiling methods have been proposed. Such methods study the behaviour of different gene modules (e.g. gene ontology terms) in response to one particular variable (e.g. differential gene expression). In spite to the wealth of information provided by functional profiling methods, a common limitation to all of them is their inherent unidimensional nature. In order to overcome this restriction we present a multidimensional logistic model that allows studying the relationship of gene modules with different genome-scale measurements (e.g. differential expression, genotyping association, methylation, copy number alterations, heterozygosity, etc.) simultaneously. Moreover, the relationship of such functional modules with the interactions among the variables can also be studied, which produces novel results impossible to be derived from the conventional unidimensional functional profiling methods. We report sound results of gene sets associations that remained undetected by the conventional one-dimensional gene set analysis in several examples. Our findings demonstrate the potential of the proposed approach for the discovery of new cell functionalities with complex dependences on more than one variable.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/20436964?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%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Formulating and testing hypotheses in functional genomics.</style></title><secondary-title><style face="normal" font="default" size="100%">Artif Intell Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Artif Intell Med</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biological Evolution</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Genotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Theoretical</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009 Feb-Mar</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">45</style></volume><pages><style face="normal" font="default" size="100%">97-107</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;OBJECTIVE: &lt;/b&gt;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.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS AND RESULTS: &lt;/b&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2-3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/18789659?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%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Minguez, Pablo</style></author><author><style face="normal" font="default" size="100%">Al-Shahrour, Fátima</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%">Gene set internal coherence in the context of functional profiling.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Genomics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Genomics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Breast Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Carcinoma, Intraductal, Noninfiltrating</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Nucleic Acid</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</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%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Papillomavirus Infections</style></keyword><keyword><style  face="normal" font="default" size="100%">Reproducibility of Results</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009 Apr 27</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">197</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;Functional profiling methods have been extensively used in the context of high-throughput experiments and, in particular, in microarray data analysis. Such methods use available biological information to define different types of functional gene modules (e.g. gene ontology -GO-, KEGG pathways, etc.) whose representation in a pre-defined list of genes is further studied. In the most popular type of microarray experimental designs (e.g. up- or down-regulated genes, clusters of co-expressing genes, etc.) or in other genomic experiments (e.g. Chip-on-chip, epigenomics, etc.) these lists are composed by genes with a high degree of co-expression. Therefore, an implicit assumption in the application of functional profiling methods within this context is that the genes corresponding to the modules tested are effectively defining sets of co-expressing genes. Nevertheless not all the functional modules are biologically coherent entities in terms of co-expression, which will eventually hinder its detection with conventional methods of functional enrichment.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Using a large collection of microarray data we have carried out a detailed survey of internal correlation in GO terms and KEGG pathways, providing a coherence index to be used for measuring functional module co-regulation. An unexpected low level of internal correlation was found among the modules studied. Only around 30% of the modules defined by GO terms and 57% of the modules defined by KEGG pathways display an internal correlation higher than the expected by chance.This information on the internal correlation of the genes within the functional modules can be used in the context of a logistic regression model in a simple way to improve their detection in gene expression experiments.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;For the first time, an exhaustive study on the internal co-expression of the most popular functional categories has been carried out. Interestingly, the real level of coexpression within many of them is lower than expected (or even inexistent), which will preclude its detection by means of most conventional functional profiling methods. If the gene-to-function correlation information is used in functional profiling methods, the results obtained improve the ones obtained by conventional enrichment methods.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/19397819?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%">Götz, Stefan</style></author><author><style face="normal" font="default" size="100%">García-Gómez, Juan Miguel</style></author><author><style face="normal" font="default" size="100%">Terol, Javier</style></author><author><style face="normal" font="default" size="100%">Williams, Tim D</style></author><author><style face="normal" font="default" size="100%">Nagaraj, Shivashankar H</style></author><author><style face="normal" font="default" size="100%">Nueda, Maria José</style></author><author><style face="normal" font="default" size="100%">Robles, Montserrat</style></author><author><style face="normal" font="default" size="100%">Talon, Manuel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Conesa, Ana</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">High-throughput functional annotation and data mining with the Blast2GO suite.</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%">Animals</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%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Expressed Sequence Tags</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, Protein</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Vocabulary, Controlled</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008 Jun</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">3420-35</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Functional genomics technologies have been widely adopted in the biological research of both model and non-model species. An efficient functional annotation of DNA or protein sequences is a major requirement for the successful application of these approaches as functional information on gene products is often the key to the interpretation of experimental results. Therefore, there is an increasing need for bioinformatics resources which are able to cope with large amount of sequence data, produce valuable annotation results and are easily accessible to laboratories where functional genomics projects are being undertaken. We present the Blast2GO suite as an integrated and biologist-oriented solution for the high-throughput and automatic functional annotation of DNA or protein sequences based on the Gene Ontology vocabulary. The most outstanding Blast2GO features are: (i) the combination of various annotation strategies and tools controlling type and intensity of annotation, (ii) the numerous graphical features such as the interactive GO-graph visualization for gene-set function profiling or descriptive charts, (iii) the general sequence management features and (iv) high-throughput capabilities. We used the Blast2GO framework to carry out a detailed analysis of annotation behaviour through homology transfer and its impact in functional genomics research. Our aim is to offer biologists useful information to take into account when addressing the task of functionally characterizing their sequence data.&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/18445632?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%">Huerta-Cepas, Jaime</style></author><author><style face="normal" font="default" size="100%">Bueno, Anibal</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Gabaldón, Toni</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">PhylomeDB: a database for genome-wide collections of gene phylogenies.</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%">Base Sequence</style></keyword><keyword><style  face="normal" font="default" size="100%">Escherichia coli</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">History, Ancient</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Phylogeny</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Saccharomyces cerevisiae</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Alignment</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008 Jan</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">D491-6</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 complete collection of evolutionary histories of all genes in a genome, also known as phylome, constitutes a valuable source of information. The reconstruction of phylomes has been previously prevented by large demands of time and computer power, but is now feasible thanks to recent developments in computers and algorithms. To provide a publicly available repository of complete phylomes that allows researchers to access and store large-scale phylogenomic analyses, we have developed PhylomeDB. PhylomeDB is a database of complete phylomes derived for different genomes within a specific taxonomic range. All phylomes in the database are built using a high-quality phylogenetic pipeline that includes evolutionary model testing and alignment trimming phases. For each genome, PhylomeDB provides the alignments, phylogentic trees and tree-based orthology predictions for every single encoded protein. The current version of PhylomeDB includes the phylomes of Human, the yeast Saccharomyces cerevisiae and the bacterium Escherichia coli, comprising a total of 32 289 seed sequences with their corresponding alignments and 172 324 phylogenetic trees. PhylomeDB can be publicly accessed at http://phylomedb.bioinfo.cipf.es.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">Database issue</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/17962297?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%">Al-Shahrour, Fátima</style></author><author><style face="normal" font="default" size="100%">Minguez, Pablo</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%">Alloza, Eva</style></author><author><style face="normal" font="default" size="100%">Montaner, David</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%">FatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments.</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%">Amino Acid Motifs</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Binding Sites</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes</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%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Programming Languages</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems Integration</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2007 Jul</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">W91-6</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 ultimate goal of any genome-scale experiment is to provide a functional interpretation of the data, relating the available information with the hypotheses that originated the experiment. Thus, functional profiling methods have become essential in diverse scenarios such as microarray experiments, proteomics, etc. We present the FatiGO+, a web-based tool for the functional profiling of genome-scale experiments, specially oriented to the interpretation of microarray experiments. In addition to different functional annotations (gene ontology, KEGG pathways, Interpro motifs, Swissprot keywords and text-mining based bioentities related to diseases and chemical compounds) FatiGO+ includes, as a novelty, regulatory and structural information. The regulatory information used includes predictions of targets for distinct regulatory elements (obtained from the Transfac and CisRed databases). Additionally FatiGO+ uses predictions of target motifs of miRNA to infer which of these can be activated or deactivated in the sample of genes studied. Finally, properties of gene products related to their relative location and connections in the interactome have also been used. Also, enrichment of any of these functional terms can be directly analysed on chromosomal coordinates. FatiGO+ can be found at: http://www.fatigoplus.org and within the Babelomics environment http://www.babelomics.org.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">Web Server issue</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/17478504?dopt=Abstract</style></custom1></record></records></xml>