<?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%">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%">Galvez, Juan M</style></author><author><style face="normal" font="default" size="100%">Castillo-Secilla, Daniel</style></author><author><style face="normal" font="default" size="100%">Herrera, Luis J</style></author><author><style face="normal" font="default" size="100%">Valenzuela, Olga</style></author><author><style face="normal" font="default" size="100%">Caba, Octavio</style></author><author><style face="normal" font="default" size="100%">Prados, Jose C</style></author><author><style face="normal" font="default" size="100%">Ortuno, Francisco M</style></author><author><style face="normal" font="default" size="100%">Rojas, Ignacio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Improving Skin Cancer Diagnosis by Integrating Microarray and RNA-Seq Datasets.</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE J Biomed Health Inform</style></secondary-title><alt-title><style face="normal" font="default" size="100%">IEEE J Biomed Health Inform</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biomarkers, Tumor</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Diagnosis, Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</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%">RNA-seq</style></keyword><keyword><style  face="normal" font="default" size="100%">Skin Neoplasms</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 07</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">24</style></volume><pages><style face="normal" font="default" size="100%">2119-2130</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Many clinical studies have revealed the high biological similarities existing among different skin pathological states. These similarities create difficulties in the efficient diagnosis of skin cancer, and encourage to study and design new intelligent clinical decision support systems. In this sense, gene expression analysis can help find differentially expressed genes (DEGs) simultaneously discerning multiple skin pathological states in a single test. The integration of multiple heterogeneous transcriptomic datasets requires different pipeline stages to be properly designed: from suitable batch merging and efficient biomarker selection to automated classification assessment. This article presents a novel approach addressing all these technical issues, with the intention of providing new sights about skin cancer diagnosis. Although new future efforts will have to be made in the search for better biomarkers recognizing specific skin pathological states, our study found a panel of 8 highly relevant multiclass DEGs for discerning up to 10 skin pathological states: 2 healthy skin conditions a priori, 2 cataloged precancerous skin diseases and 6 cancerous skin states. Their power of diagnosis over new samples was widely tested by previously well-trained classification models. Robust performance metrics such as overall and mean multiclass F1-score outperformed recognition rates of 94% and 80%, respectively. Clinicians should give special attention to highlighted multiclass DEGs that have high gene expression changes present among them, and understand their biological relationship to different skin pathological states.&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/31871000?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%">Chacón-Solano, E</style></author><author><style face="normal" font="default" size="100%">León, C</style></author><author><style face="normal" font="default" size="100%">Díaz, F</style></author><author><style face="normal" font="default" size="100%">García-García, F</style></author><author><style face="normal" font="default" size="100%">García, M</style></author><author><style face="normal" font="default" size="100%">Escámez, M J</style></author><author><style face="normal" font="default" size="100%">Guerrero-Aspizua, S</style></author><author><style face="normal" font="default" size="100%">Conti, C J</style></author><author><style face="normal" font="default" size="100%">Mencía, Á</style></author><author><style face="normal" font="default" size="100%">Martínez-Santamaría, L</style></author><author><style face="normal" font="default" size="100%">Llames, S</style></author><author><style face="normal" font="default" size="100%">Pévida, M</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, J</style></author><author><style face="normal" font="default" size="100%">Puig-Butillé, J A</style></author><author><style face="normal" font="default" size="100%">Maseda, R</style></author><author><style face="normal" font="default" size="100%">Puig, S</style></author><author><style face="normal" font="default" size="100%">de Lucas, R</style></author><author><style face="normal" font="default" size="100%">Baselga, E</style></author><author><style face="normal" font="default" size="100%">Larcher, F</style></author><author><style face="normal" font="default" size="100%">Dopazo, J</style></author><author><style face="normal" font="default" size="100%">Del Rio, M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fibroblast activation and abnormal extracellular matrix remodelling as common hallmarks in three cancer-prone genodermatoses.</style></title><secondary-title><style face="normal" font="default" size="100%">Br J Dermatol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Br J Dermatol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adolescent</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Biopsy</style></keyword><keyword><style  face="normal" font="default" size="100%">Blister</style></keyword><keyword><style  face="normal" font="default" size="100%">Case-Control Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Cells, Cultured</style></keyword><keyword><style  face="normal" font="default" size="100%">Child</style></keyword><keyword><style  face="normal" font="default" size="100%">Child, Preschool</style></keyword><keyword><style  face="normal" font="default" size="100%">Epidermolysis Bullosa</style></keyword><keyword><style  face="normal" font="default" size="100%">Epidermolysis Bullosa Dystrophica</style></keyword><keyword><style  face="normal" font="default" size="100%">Extracellular Matrix</style></keyword><keyword><style  face="normal" font="default" size="100%">Extracellular Matrix Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Fibroblasts</style></keyword><keyword><style  face="normal" font="default" size="100%">Fibrosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Healthy Volunteers</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Infant</style></keyword><keyword><style  face="normal" font="default" size="100%">Infant, Newborn</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Periodontal Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Photosensitivity Disorders</style></keyword><keyword><style  face="normal" font="default" size="100%">Primary Cell Culture</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA-seq</style></keyword><keyword><style  face="normal" font="default" size="100%">Skin</style></keyword><keyword><style  face="normal" font="default" size="100%">Xeroderma Pigmentosum</style></keyword><keyword><style  face="normal" font="default" size="100%">Young Adult</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 09</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">181</style></volume><pages><style face="normal" font="default" size="100%">512-522</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;Recessive dystrophic epidermolysis bullosa (RDEB), Kindler syndrome (KS) and xeroderma pigmentosum complementation group C (XPC) are three cancer-prone genodermatoses whose causal genetic mutations cannot fully explain, on their own, the array of associated phenotypic manifestations. Recent evidence highlights the role of the stromal microenvironment in the pathology of these disorders.&lt;/p&gt;&lt;p&gt;&lt;b&gt;OBJECTIVES: &lt;/b&gt;To investigate, by means of comparative gene expression analysis, the role played by dermal fibroblasts in the pathogenesis of RDEB, KS and XPC.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We conducted RNA-Seq analysis, which included a thorough examination of the differentially expressed genes, a functional enrichment analysis and a description of affected signalling circuits. Transcriptomic data were validated at the protein level in cell cultures, serum samples and skin biopsies.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Interdisease comparisons against control fibroblasts revealed a unifying signature of 186 differentially expressed genes and four signalling pathways in the three genodermatoses. Remarkably, some of the uncovered expression changes suggest a synthetic fibroblast phenotype characterized by the aberrant expression of extracellular matrix (ECM) proteins. Western blot and immunofluorescence in situ analyses validated the RNA-Seq data. In addition, enzyme-linked immunosorbent assay revealed increased circulating levels of periostin in patients with RDEB.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Our results suggest that the different causal genetic defects converge into common changes in gene expression, possibly due to injury-sensitive events. These, in turn, trigger a cascade of reactions involving abnormal ECM deposition and underexpression of antioxidant enzymes. The elucidated expression signature provides new potential biomarkers and common therapeutic targets in RDEB, XPC and KS. What's already known about this topic? Recessive dystrophic epidermolysis bullosa (RDEB), Kindler syndrome (KS) and xeroderma pigmentosum complementation group C (XPC) are three genodermatoses with high predisposition to cancer development. Although their causal genetic mutations mainly affect epithelia, the dermal microenvironment likely contributes to the physiopathology of these disorders. What does this study add? We disclose a large overlapping transcription profile between XPC, KS and RDEB fibroblasts that points towards an activated phenotype with high matrix-synthetic capacity. This common signature seems to be independent of the primary causal deficiency, but reflects an underlying derangement of the extracellular matrix via transforming growth factor-β signalling activation and oxidative state imbalance. What is the translational message? This study broadens the current knowledge about the pathology of these diseases and highlights new targets and biomarkers for effective therapeutic intervention. It is suggested that high levels of circulating periostin could represent a potential biomarker in RDEB.&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/30693469?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%">Alonso, Roberto</style></author><author><style face="normal" font="default" size="100%">Salavert, Francisco</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Bleda, Marta</style></author><author><style face="normal" font="default" size="100%">García-Alonso, Luz</style></author><author><style face="normal" font="default" size="100%">Sanchis-Juan, Alba</style></author><author><style face="normal" font="default" size="100%">Perez-Gil, Daniel</style></author><author><style face="normal" font="default" size="100%">Marin-Garcia, Pablo</style></author><author><style face="normal" font="default" size="100%">Sánchez, Rubén</style></author><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%">Hernansaiz-Ballesteros, Rosa D</style></author><author><style face="normal" font="default" size="100%">Alemán, Alejandro</style></author><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Montaner, David</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%">Babelomics 5.0: functional interpretation for new generations of genomic data.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic acids research</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">babelomics</style></keyword><keyword><style  face="normal" font="default" size="100%">data integration</style></keyword><keyword><style  face="normal" font="default" size="100%">gene set analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">interactome</style></keyword><keyword><style  face="normal" font="default" size="100%">network analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA-seq</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems biology</style></keyword><keyword><style  face="normal" font="default" size="100%">transcriptomics</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 Apr 20</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://nar.oxfordjournals.org/content/43/W1/W117</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">W1</style></number><volume><style face="normal" font="default" size="100%">43</style></volume><pages><style face="normal" font="default" size="100%">W117-W121</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Babelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome resequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at: http://www.babelomics.org.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Munro, Sarah A</style></author><author><style face="normal" font="default" size="100%">Lund, Steven P</style></author><author><style face="normal" font="default" size="100%">Pine, P Scott</style></author><author><style face="normal" font="default" size="100%">Binder, Hans</style></author><author><style face="normal" font="default" size="100%">Clevert, Djork-Arné</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Fasold, Mario</style></author><author><style face="normal" font="default" size="100%">Hochreiter, Sepp</style></author><author><style face="normal" font="default" size="100%">Hong, Huixiao</style></author><author><style face="normal" font="default" size="100%">Jafari, Nadereh</style></author><author><style face="normal" font="default" size="100%">Kreil, David P</style></author><author><style face="normal" font="default" size="100%">Labaj, Paweł P</style></author><author><style face="normal" font="default" size="100%">Li, Sheng</style></author><author><style face="normal" font="default" size="100%">Liao, Yang</style></author><author><style face="normal" font="default" size="100%">Lin, Simon M</style></author><author><style face="normal" font="default" size="100%">Meehan, Joseph</style></author><author><style face="normal" font="default" size="100%">Mason, Christopher E</style></author><author><style face="normal" font="default" size="100%">Santoyo-López, Javier</style></author><author><style face="normal" font="default" size="100%">Setterquist, Robert A</style></author><author><style face="normal" font="default" size="100%">Shi, Leming</style></author><author><style face="normal" font="default" size="100%">Shi, Wei</style></author><author><style face="normal" font="default" size="100%">Smyth, Gordon K</style></author><author><style face="normal" font="default" size="100%">Stralis-Pavese, Nancy</style></author><author><style face="normal" font="default" size="100%">Su, Zhenqiang</style></author><author><style face="normal" font="default" size="100%">Tong, Weida</style></author><author><style face="normal" font="default" size="100%">Wang, Charles</style></author><author><style face="normal" font="default" size="100%">Wang, Jian</style></author><author><style face="normal" font="default" size="100%">Xu, Joshua</style></author><author><style face="normal" font="default" size="100%">Ye, Zhan</style></author><author><style face="normal" font="default" size="100%">Yang, Yong</style></author><author><style face="normal" font="default" size="100%">Yu, Ying</style></author><author><style face="normal" font="default" size="100%">Salit, Marc</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures.</style></title><secondary-title><style face="normal" font="default" size="100%">Nature communications</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">RNA-seq</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</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.nature.com/ncomms/2014/140925/ncomms6125/full/ncomms6125.html</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">5125</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">There is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance with a proposed standard ’dashboard’ of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias. The performance metrics suite is applicable to analysis of a typical experiment, and here we also apply these metrics to evaluate technical performance among laboratories. An interlaboratory study using identical samples shared among 12 laboratories with three different measurement processes demonstrates generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias are also comparable among laboratories for the same measurement process. We observe different biases for measurement processes using different mRNA-enrichment protocols.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Su, Z.</style></author><author><style face="normal" font="default" size="100%">Labaj, P.P.</style></author><author><style face="normal" font="default" size="100%">....</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">....</style></author><author><style face="normal" font="default" size="100%">Mason, C.E.</style></author><author><style face="normal" font="default" size="100%">Shi, L</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium.</style></title><secondary-title><style face="normal" font="default" size="100%">Nature biotechnology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA-seq</style></keyword><keyword><style  face="normal" font="default" size="100%">SEQC</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014 Aug 24</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.2957.html</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">903–914</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with &gt;80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising &gt;100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.</style></abstract></record></records></xml>