<?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%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Hidalgo, Marta R</style></author><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">Pujana, Miguel A</style></author><author><style face="normal" font="default" size="100%">Mateo, Francesca</style></author><author><style face="normal" font="default" size="100%">Herranz, Carmen</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape.</style></title><secondary-title><style face="normal" font="default" size="100%">Cancer Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Cancer Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cell Line, Tumor</style></keyword><keyword><style  face="normal" font="default" size="100%">Cluster Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease Progression</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, Neoplastic</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Kaplan-Meier Estimate</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolome</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Oncogenes</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Prognosis</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Small Interfering</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, RNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword><keyword><style  face="normal" font="default" size="100%">Treatment Outcome</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 11 01</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">78</style></volume><pages><style face="normal" font="default" size="100%">6059-6072</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Metabolic reprogramming plays an important role in cancer development and progression and is a well-established hallmark of cancer. Despite its inherent complexity, cellular metabolism can be decomposed into functional modules that represent fundamental metabolic processes. Here, we performed a pan-cancer study involving 9,428 samples from 25 cancer types to reveal metabolic modules whose individual or coordinated activity predict cancer type and outcome, in turn highlighting novel therapeutic opportunities. Integration of gene expression levels into metabolic modules suggests that the activity of specific modules differs between cancers and the corresponding tissues of origin. Some modules may cooperate, as indicated by the positive correlation of their activity across a range of tumors. The activity of many metabolic modules was significantly associated with prognosis at a stronger magnitude than any of their constituent genes. Thus, modules may be classified as tumor suppressors and oncomodules according to their potential impact on cancer progression. Using this modeling framework, we also propose novel potential therapeutic targets that constitute alternative ways of treating cancer by inhibiting their reprogrammed metabolism. Collectively, this study provides an extensive resource of predicted cancer metabolic profiles and dependencies. Combining gene expression with metabolic modules identifies molecular mechanisms of cancer undetected on an individual gene level and allows discovery of new potential therapeutic targets. .&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">21</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30135189?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%">Gonzalez, Sergio</style></author><author><style face="normal" font="default" size="100%">Clavijo, Bernardo</style></author><author><style face="normal" font="default" size="100%">Rivarola, Máximo</style></author><author><style face="normal" font="default" size="100%">Moreno, Patricio</style></author><author><style face="normal" font="default" size="100%">Fernandez, Paula</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Paniego, Norma</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ATGC transcriptomics: a web-based application to integrate, explore and analyze de novo transcriptomic data.</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%">Animals</style></keyword><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%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, RNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</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%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Feb 22</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">121</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 the last years, applications based on massively parallelized RNA sequencing (RNA-seq) have become valuable approaches for studying non-model species, e.g., without a fully sequenced genome. RNA-seq is a useful tool for detecting novel transcripts and genetic variations and for evaluating differential gene expression by digital measurements. The large and complex datasets resulting from functional genomic experiments represent a challenge in data processing, management, and analysis. This problem is especially significant for small research groups working with non-model species.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We developed a web-based application, called ATGC transcriptomics, with a flexible and adaptable interface that allows users to work with new generation sequencing (NGS) transcriptomic analysis results using an ontology-driven database. This new application simplifies data exploration, visualization, and integration for a better comprehension of the results.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;ATGC transcriptomics provides access to non-expert computer users and small research groups to a scalable storage option and simple data integration, including database administration and management. The software is freely available under the terms of GNU public license at http://atgcinta.sourceforge.net .&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/28222698?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%">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%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">Salavert, Francisco</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes.</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%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">gene expression</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Precision Medicine</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, RNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</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 Jan 17</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">5160-5178</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions.&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/28042959?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%">Medina, I</style></author><author><style face="normal" font="default" size="100%">Tárraga, J</style></author><author><style face="normal" font="default" size="100%">Martínez, H</style></author><author><style face="normal" font="default" size="100%">Barrachina, S</style></author><author><style face="normal" font="default" size="100%">Castillo, M I</style></author><author><style face="normal" font="default" size="100%">Paschall, J</style></author><author><style face="normal" font="default" size="100%">Salavert-Torres, J</style></author><author><style face="normal" font="default" size="100%">Blanquer-Espert, I</style></author><author><style face="normal" font="default" size="100%">Hernández-García, V</style></author><author><style face="normal" font="default" size="100%">Quintana-Ortí, E S</style></author><author><style face="normal" font="default" size="100%">Dopazo, J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Highly sensitive and ultrafast read mapping for RNA-seq analysis.</style></title><secondary-title><style face="normal" font="default" size="100%">DNA Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">DNA Res</style></alt-title></titles><keywords><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%">Sensitivity and Specificity</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, RNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</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 Apr</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">23</style></volume><pages><style face="normal" font="default" size="100%">93-100</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;As sequencing technologies progress, the amount of data produced grows exponentially, shifting the bottleneck of discovery towards the data analysis phase. In particular, currently available mapping solutions for RNA-seq leave room for improvement in terms of sensitivity and performance, hindering an efficient analysis of transcriptomes by massive sequencing. Here, we present an innovative approach that combines re-engineering, optimization and parallelization. This solution results in a significant increase of mapping sensitivity over a wide range of read lengths and substantial shorter runtimes when compared with current RNA-seq mapping methods 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/26740642?dopt=Abstract</style></custom1></record></records></xml>