<?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%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Panadero, Joaquin</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Integrated gene set analysis for microRNA studies.</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%">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%">Gene ontology</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</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%">MicroRNAs</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasms</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%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 09 15</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">2809-16</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;Functional interpretation of miRNA expression data is currently done in a three step procedure: select differentially expressed miRNAs, find their target genes, and carry out gene set overrepresentation analysis Nevertheless, major limitations of this approach have already been described at the gene level, while some newer arise in the miRNA scenario.Here, we propose an enhanced methodology that builds on the well-established gene set analysis paradigm. Evidence for differential expression at the miRNA level is transferred to a gene differential inhibition score which is easily interpretable in terms of gene sets or pathways. Such transferred indexes account for the additive effect of several miRNAs targeting the same gene, and also incorporate cancellation effects between cases and controls. Together, these two desirable characteristics allow for more accurate modeling of regulatory processes.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We analyze high-throughput sequencing data from 20 different cancer types and provide exhaustive reports of gene and Gene Ontology-term deregulation by miRNA action.&lt;/p&gt;&lt;p&gt;&lt;b&gt;AVAILABILITY AND IMPLEMENTATION: &lt;/b&gt;The proposed methodology was implemented in the Bioconductor library mdgsa http://bioconductor.org/packages/mdgsa For the purpose of reproducibility all of the scripts are available at https://github.com/dmontaner-papers/gsa4mirna&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONTACT: &lt;/b&gt;: david.montaner@gmail.com&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%">18</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/27324197?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%">González-Pérez, Sergio</style></author><author><style face="normal" font="default" size="100%">Gutiérrez, Jorge</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Osuna, Daniel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Lorenzo, Oscar</style></author><author><style face="normal" font="default" size="100%">Revuelta, José L</style></author><author><style face="normal" font="default" size="100%">Arellano, Juan B</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Early transcriptional defense responses in Arabidopsis cell suspension culture under high-light conditions.</style></title><secondary-title><style face="normal" font="default" size="100%">Plant Physiol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Plant Physiol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Arabidopsis</style></keyword><keyword><style  face="normal" font="default" size="100%">Blotting, Western</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Culture Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">Cells, Cultured</style></keyword><keyword><style  face="normal" font="default" size="100%">Chloroplasts</style></keyword><keyword><style  face="normal" font="default" size="100%">Cluster Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Hydrogen Peroxide</style></keyword><keyword><style  face="normal" font="default" size="100%">Light</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Photosystem II Protein Complex</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant Growth Regulators</style></keyword><keyword><style  face="normal" font="default" size="100%">Reproducibility of Results</style></keyword><keyword><style  face="normal" font="default" size="100%">Reverse Transcriptase Polymerase Chain Reaction</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Messenger</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Stress, Physiological</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription, Genetic</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%">156</style></volume><pages><style face="normal" font="default" size="100%">1439-56</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 early transcriptional defense responses and reactive oxygen species (ROS) production in Arabidopsis (Arabidopsis thaliana) cell suspension culture (ACSC), containing functional chloroplasts, were examined at high light (HL). The transcriptional analysis revealed that most of the ROS markers identified among the 449 transcripts with significant differential expression were transcripts specifically up-regulated by singlet oxygen ((1)O(2)). On the contrary, minimal correlation was established with transcripts specifically up-regulated by superoxide radical or hydrogen peroxide. The transcriptional analysis was supported by fluorescence microscopy experiments. The incubation of ACSC with the (1)O(2) sensor green reagent and 2',7'-dichlorofluorescein diacetate showed that the 30-min-HL-treated cultures emitted fluorescence that corresponded with the production of (1)O(2) but not of hydrogen peroxide. Furthermore, the in vivo photodamage of the D1 protein of photosystem II indicated that the photogeneration of (1)O(2) took place within the photosystem II reaction center. Functional enrichment analyses identified transcripts that are key components of the ROS signaling transduction pathway in plants as well as others encoding transcription factors that regulate both ROS scavenging and water deficit stress. A meta-analysis examining the transcriptional profiles of mutants and hormone treatments in Arabidopsis showed a high correlation between ACSC at HL and the fluorescent mutant family of Arabidopsis, a producer of (1)O(2) in plastids. Intriguingly, a high correlation was also observed with ABA deficient1 and more axillary growth4, two mutants with defects in the biosynthesis pathways of two key (apo)carotenoid-derived plant hormones (i.e. abscisic acid and strigolactones, respectively). ACSC has proven to be a valuable system for studying early transcriptional responses to HL stress.&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/21531897?dopt=Abstract</style></custom1></record></records></xml>