<?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%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Gallego, Asunción</style></author><author><style face="normal" font="default" size="100%">Arnau, Vicente</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%">HPG pore: an efficient and scalable framework for nanopore sequencing data.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC bioinformatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">hadoop</style></keyword><keyword><style  face="normal" font="default" size="100%">HPC</style></keyword><keyword><style  face="normal" font="default" size="100%">nanopore</style></keyword><keyword><style  face="normal" font="default" size="100%">NGS</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</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.biomedcentral.com/1471-2105/17/107</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">107</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">BACKGROUND: The use of nanopore technologies is expected to spread in the future because they are portable and can sequence long fragments of DNA molecules without prior amplification. The first nanopore sequencer available, the MinION™ from Oxford Nanopore Technologies, is a USB-connected, portable device that allows real-time DNA analysis. In addition, other new instruments are expected to be released soon, which promise to outperform the current short-read technologies in terms of throughput. Despite the flood of data expected from this technology, the data analysis solutions currently available are only designed to manage small projects and are not scalable. RESULTS: Here we present HPG Pore, a toolkit for exploring and analysing nanopore sequencing data. HPG Pore can run on both individual computers and in the Hadoop distributed computing framework, which allows easy scale-up to manage the large amounts of data expected to result from extensive use of nanopore technologies in the future. CONCLUSIONS: HPG Pore allows for virtually unlimited sequencing data scalability, thus guaranteeing its continued management in near future scenarios. HPG Pore is available in GitHub at http://github.com/opencb/hpg-pore .</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%">Hernansaiz-Ballesteros, Rosa D</style></author><author><style face="normal" font="default" size="100%">Salavert, Francisco</style></author><author><style face="normal" font="default" size="100%">Sebastián-Leon, Patricia</style></author><author><style face="normal" font="default" size="100%">Alemán, Alejandro</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessing the impact of mutations found in next generation sequencing data over human signaling pathways.</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%">NGS</style></keyword><keyword><style  face="normal" font="default" size="100%">pathways</style></keyword><keyword><style  face="normal" font="default" size="100%">signalling</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems biology</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 16</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://nar.oxfordjournals.org/content/43/W1/W270</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%">W270-W275</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Modern sequencing technologies produce increasingly detailed data on genomic variation. However, conventional methods for relating either individual variants or mutated genes to phenotypes present known limitations given the complex, multigenic nature of many diseases or traits. Here we present PATHiVar, a web-based tool that integrates genomic variation data with gene expression tissue information. PATHiVar constitutes a new generation of genomic data analysis methods that allow studying variants found in next generation sequencing experiment in the context of signaling pathways. Simple Boolean models of pathways provide detailed descriptions of the impact of mutations in cell functionality so as, recurrences in functionality failures can easily be related to diseases, even if they are produced by mutations in different genes. Patterns of changes in signal transmission circuits, often unpredictable from individual genes mutated, correspond to patterns of affected functionalities that can be related to complex traits such as disease progression, drug response, etc. PATHiVar is available at: http://pathivar.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%">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%">Martinez, Hector</style></author><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Barrachina, Sergio</style></author><author><style face="normal" font="default" size="100%">Castillo, Maribel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Quintana-Orti, Enrique S</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Concurrent and Accurate Short Read Mapping on Multicore Processors.</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">HPC</style></keyword><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword><keyword><style  face="normal" font="default" size="100%">short real mapping</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015 Sep-Oct</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&amp;arnumber=7010005</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">995-1007</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We introduce a parallel aligner with a work-flow organization for fast and accurate mapping of RNA sequences on servers equipped with multicore processors. Our software, [Formula: see text] ([Formula: see text] is an open-source application. The software is available at http://www.opencb.org, exploits a suffix array to rapidly map a large fraction of the RNA fragments (reads), as well as leverages the accuracy of the Smith-Waterman algorithm to deal with conflictive reads. The aligner is enhanced with a careful strategy to detect splice junctions based on an adaptive division of RNA reads into small segments (or seeds), which are then mapped onto a number of candidate alignment locations, providing crucial information for the successful alignment of the complete reads. The experimental results on a platform with Intel multicore technology report the parallel performance of [Formula: see text], on RNA reads of 100-400 nucleotides, which excels in execution time/sensitivity to state-of-the-art aligners such as TopHat 2+Bowtie 2, MapSplice, and STAR.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Pérez, Mariano</style></author><author><style face="normal" font="default" size="100%">Orduña, Juan M</style></author><author><style face="normal" font="default" size="100%">Duato, José</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Parallel and Sensitive Software Tool for Methylation Analysis on Multicore Platforms.</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics (Oxford, England)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">BS-seq</style></keyword><keyword><style  face="normal" font="default" size="100%">HPC</style></keyword><keyword><style  face="normal" font="default" size="100%">methylation</style></keyword><keyword><style  face="normal" font="default" size="100%">NGS</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 Jun 10</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://bioinformatics.oxfordjournals.org/content/31/19/3130.long</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">19</style></number><volume><style face="normal" font="default" size="100%">31</style></volume><pages><style face="normal" font="default" size="100%">3130-3138</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">MOTIVATION: DNA methylation analysis suffers from very long processing time, since the advent of Next-Generation Sequencers (NGS) has shifted the bottleneck of genomic studies from the sequencers that obtain the DNA samples to the software that performs the analysis of these samples. The existing software for methylation analysis does not seem to scale efficiently neither with the size of the dataset nor with the length of the reads to be analyzed. Since it is expected that the sequencers will provide longer and longer reads in the near future, efficient and scalable methylation software should be developed. RESULTS: We present a new software tool, called HPG-Methyl, which efficiently maps bisulfite sequencing reads on DNA, analyzing DNA methylation. The strategy used by this software consists of leveraging the speed of the Burrows-Wheeler Transform to map a large number of DNA fragments (reads) rapidly, as well as the accuracy of the Smith-Waterman algorithm, which is exclusively employed to deal with the most ambiguous and shortest reads. Experimental results on platforms with Intel multicore processors show that HPGMethyl significantly outperforms in both execution time and sensitivity state-of-the-art software such as Bismark, BS-Seeker or BSMAP, particularly for long bisulfite reads. AVAILABILITY: Software in the form of C libraries and functions, together with instructions to compile and execute this software. Available by sftp to anonymous@clariano.uv.es (password &quot;anonymous&quot;). CONTACT: Juan.Orduna@uv.es.</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%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Arnau, Vicente</style></author><author><style face="normal" font="default" size="100%">Martinez, Hector</style></author><author><style face="normal" font="default" size="100%">Moreno, Raul</style></author><author><style face="normal" font="default" size="100%">Cazorla, Diego</style></author><author><style face="normal" font="default" size="100%">Salavert-Torres, José</style></author><author><style face="normal" font="default" size="100%">Blanquer-Espert, Ignacio</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Acceleration of short and long DNA read mapping without loss of accuracy using suffix array.</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics (Oxford, England)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword><keyword><style  face="normal" font="default" size="100%">short read mapping. HPC. suffix arrays</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 20</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://bioinformatics.oxfordjournals.org/content/early/2014/08/19/bioinformatics.btu553.long</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">30</style></volume><pages><style face="normal" font="default" size="100%">3396-3398</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">HPG Aligner applies suffix arrays for DNA read mapping. This implementation produces a highly sensitive and extremely fast mapping of DNA reads that scales up almost linearly with read length. The approach presented here is faster (over 20x for long reads) and more sensitive (over 98% in a wide range of read lengths) than the current, state-of-the-art mappers. HPG Aligner is not only an optimal alternative for current sequencers but also the only solution available to cope with longer reads and growing throughputs produced by forthcoming sequencing technologies.</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%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Salavert, Francisco</style></author><author><style face="normal" font="default" size="100%">Sánchez, Rubén</style></author><author><style face="normal" font="default" size="100%">De Maria, Alejandro</style></author><author><style face="normal" font="default" size="100%">Alonso, Roberto</style></author><author><style face="normal" font="default" size="100%">Escobar, Pablo</style></author><author><style face="normal" font="default" size="100%">Bleda, Marta</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genome Maps, a new generation genome browser.</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%">BAM</style></keyword><keyword><style  face="normal" font="default" size="100%">genome viewer</style></keyword><keyword><style  face="normal" font="default" size="100%">HTML5</style></keyword><keyword><style  face="normal" font="default" size="100%">javascript</style></keyword><keyword><style  face="normal" font="default" size="100%">Next Generation Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword><keyword><style  face="normal" font="default" size="100%">SVG</style></keyword><keyword><style  face="normal" font="default" size="100%">VCF</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013 Jun 8</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://nar.oxfordjournals.org/content/41/W1/W41</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">W1</style></number><volume><style face="normal" font="default" size="100%">41</style></volume><pages><style face="normal" font="default" size="100%">W41-W46</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Genome browsers have gained importance as more genomes and related genomic information become available. However, the increase of information brought about by new generation sequencing technologies is, at the same time, causing a subtle but continuous decrease in the efficiency of conventional genome browsers. Here, we present Genome Maps, a genome browser that implements an innovative model of data transfer and management. The program uses highly efficient technologies from the new HTML5 standard, such as scalable vector graphics, that optimize workloads at both server and client sides and ensure future scalability. Thus, data management and representation are entirely carried out by the browser, without the need of any Java Applet, Flash or other plug-in technology installation. Relevant biological data on genes, transcripts, exons, regulatory features, single-nucleotide polymorphisms, karyotype and so forth, are imported from web services and are available as tracks. In addition, several DAS servers are already included in Genome Maps. As a novelty, this web-based genome browser allows the local upload of huge genomic data files (e.g. VCF or BAM) that can be dynamically visualized in real time at the client side, thus facilitating the management of medical data affected by privacy restrictions. Finally, Genome Maps can easily be integrated in any web application by including only a few lines of code. Genome Maps is an open source collaborative initiative available in the GitHub repository (https://github.com/compbio-bigdata-viz/genome-maps). Genome Maps is available at: http://www.genomemaps.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%">Carbonell, José</style></author><author><style face="normal" font="default" size="100%">Alloza, Eva</style></author><author><style face="normal" font="default" size="100%">Arce, Pablo</style></author><author><style face="normal" font="default" size="100%">Borrego, Salud</style></author><author><style face="normal" font="default" size="100%">Santoyo, Javier</style></author><author><style face="normal" font="default" size="100%">Ruiz-Ferrer, Macarena</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Jiménez-Almazán, Jorge</style></author><author><style face="normal" font="default" size="100%">Méndez-Vidal, Cristina</style></author><author><style face="normal" font="default" size="100%">González-del Pozo, María</style></author><author><style face="normal" font="default" size="100%">Vela, Alicia</style></author><author><style face="normal" font="default" size="100%">Bhattacharya, Shomi S</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%">A map of human microRNA variation uncovers unexpectedly high levels of variability.</style></title><secondary-title><style face="normal" font="default" size="100%">Genome medicine</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">NGS</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 Aug 20</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://genomemedicine.com/content/4/8/62/abstract</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">62</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">ABSTRACT: BACKGROUND: MicroRNAs (miRNAs) are key components of the gene regulatory network in many species. During the past few years, these regulatory elements have been shown to be involved in an increasing number and range of diseases. Consequently, the compilation of a comprehensive map of natural variability in healthy population seems an obvious requirement for future research on miRNA-related pathologies. METHODS: Data on 14 populations from the 1000 Genomes Project were analysed, along with new data extracted from 60 exomes of healthy individuals from a southern Spain population, sequenced in the context of the Medical Genome Project, to derive an accurate map of miRNA variability. RESULTS: Despite the common belief that miRNAs are highly conserved elements, analysis of the sequences of the 1,152 individuals indicated that the observed level of variability is double what was expected. A total of 527 variants were found. Among these, 45 variants affected the recognition region of the corresponding miRNA and were found in 43 different miRNAs, 26 of which are known to be involved in 57 diseases. Different parts of the mature structure of the miRNA were affected to different degrees by variants, which suggests the existence of a selective pressure related to the relative functional impact of the change. Moreover, 41 variants showed a significant deviation from the Hardy-Weinberg equilibrium, which supports the existence of a selective process against some alleles. The average number of variants per individual in miRNAs was 28. CONCLUSIONS: Despite an expectation that miRNAs would be highly conserved genomic elements, our study reports a level of variability comparable to that observed for coding genes.</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%">Salavert Torres, Jose</style></author><author><style face="normal" font="default" size="100%">Blanquer Espert, Ignacio</style></author><author><style face="normal" font="default" size="100%">Tomas Dominguez, Andres</style></author><author><style face="normal" font="default" size="100%">Hernendez, Vicente</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Terraga, Joaquin</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%">Using GPUs for the Exact Alignment of Short-read Genetic Sequences by Means of the Burrows–Wheeler Transform.</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Burrows-Wheeler transform</style></keyword><keyword><style  face="normal" font="default" size="100%">CPU execution</style></keyword><keyword><style  face="normal" font="default" size="100%">GPGPU</style></keyword><keyword><style  face="normal" font="default" size="100%">NGS</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 Mar 20</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org.sire.ub.edu/xpl/articleDetails.jsp?reload=true&amp;arnumber=6175888</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">1245-1256</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">General Purpose Graphic Processing Units (GPGPUs) constitute an inexpensive resource for computing-intensive applications that could exploit an intrinsic fine-grain parallelism. This paper presents the design and implementation in GPGPUs of an exact alignment tool for nucleotide sequences based on the Burrows-Wheeler Transform. We compare this algorithm with state-of-the-art implementations of the same algorithm over standard CPUs, and considering the same conditions in terms of I/O. Excluding disk transfers, the implementation of the algorithm in GPUs shows a speedup larger than 12x, when compared to CPU execution. This implementation exploits the parallelism by concurrently searching different sequences on the same reference search tree, maximising memory locality and ensuring a symmetric access to the data. The article describes the behaviour of the algorithm in GPU, showing a good scalability in the performance, only limited by the size of the GPU inner memory.</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%">Németh, Attila</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Santoyo-López, Javier</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Péterfia, Bálint</style></author><author><style face="normal" font="default" size="100%">Solovei, Irina</style></author><author><style face="normal" font="default" size="100%">Cremer, Thomas</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Längst, Gernot</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Initial genomics of the human nucleolus.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS genetics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword><keyword><style  face="normal" font="default" size="100%">nucleolus</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</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000889</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">e1000889</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We report for the first time the genomics of a nuclear compartment of the eukaryotic cell. 454 sequencing and microarray analysis revealed the pattern of nucleolus-associated chromatin domains (NADs) in the linear human genome and identified different gene families and certain satellite repeats as the major building blocks of NADs, which constitute about 4% of the genome. Bioinformatic evaluation showed that NAD-localized genes take part in specific biological processes, like the response to other organisms, odor perception, and tissue development. 3D FISH and immunofluorescence experiments illustrated the spatial distribution of NAD-specific chromatin within interphase nuclei and its alteration upon transcriptional changes. Altogether, our findings describe the nature of DNA sequences associated with the human nucleolus and provide insights into the function of the nucleolus in genome organization and establishment of nuclear architecture.&lt;/p&gt;</style></abstract></record></records></xml>