02298nas a2200361   4500008004100000022001400041245004600055210004400101260001500145300001400160490000700174520132500181653002201506653001801528653001101546653001301557653001301570653002801583100001801611700001701629700002101646700002301667700002301690700002201713700001601735700001701751700001901768700001801787700002001805700002001825700002001845856007101865       2017                        eng d  a1362-496200aHGVA: the Human Genome Variation Archive.0 aHGVA the Human Genome Variation Archive  c2017 07 03  aW189-W1940 v453 a<p>High-profile genomic variation projects like the 1000 Genomes project or the Exome Aggregation Consortium, are generating a wealth of human genomic variation knowledge which can be used as an essential reference for identifying disease-causing genotypes. However, accessing these data, contrasting the various studies and integrating those data in downstream analyses remains cumbersome. The Human Genome Variation Archive (HGVA) tackles these challenges and facilitates access to genomic data for key reference projects in a clean, fast and integrated fashion. HGVA provides an efficient and intuitive web-interface for easy data mining, a comprehensive RESTful API and client libraries in Python, Java and JavaScript for fast programmatic access to its knowledge base. HGVA calculates population frequencies for these projects and enriches their data with variant annotation provided by CellBase, a rich and fast annotation solution. HGVA serves as a proof-of-concept of the genome analysis developments being carried out by the University of Cambridge together with UK's 100 000 genomes project and the National Institute for Health Research BioResource Rare-Diseases, in particular, deploying open-source for Computational Biology (OpenCB) software platform for storing and analyzing massive genomic datasets.</p>10aGenetic Variation10aGenome, Human10aHumans10aInternet10aSoftware10aUser-Computer Interface1 aLopez, Javier1 aColl, Jacobo1 aHaimel, Matthias1 aKandasamy, Swaathi1 aTárraga, Joaquín1 aFurio-Tari, Pedro1 aBari, Wasim1 aBleda, Marta1 aRueda, Antonio1 aGräf, Stefan1 aRendon, Augusto1 aDopazo, Joaquin1 aMedina, Ignacio  uhttps://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkx44501755nas a2200217   4500008004100000022001400041245008100055210006900136260001600205300000800221490000700229520109100236653001501327653000801342100002001350700002001370700002101390700002301411700002101434856008201455       2017                        eng d  a1471-210500aA new parallel pipeline for DNA methylation analysis of long reads datasets.0 anew parallel pipeline for DNA methylation analysis of long reads  c2017 Mar 09  a1610 v183 aBACKGROUND: DNA methylation is an important mechanism of epigenetic regulation in development and disease. New generation sequencers allow genome-wide measurements of the methylation status by reading short stretches of the DNA sequence (Methyl-seq). Several software tools for methylation analysis have been proposed over recent years. However, the current trend is that the new sequencers and the ones expected for an upcoming future yield sequences of increasing length, making these software tools inefficient and obsolete. RESULTS: In this paper, we propose a new software based on a strategy for methylation analysis of Methyl-seq sequencing data that requires much shorter execution times while yielding a better level of sensitivity, particularly for datasets composed of long reads. This strategy can be exported to other methylation, DNA and RNA analysis tools. CONCLUSIONS: The developed software tool achieves execution times one order of magnitude shorter than the existing tools, while yielding equal sensitivity for short reads and even better sensitivity for long reads.10aMethyl-Seq10aNGS1 aOlanda, Ricardo1 aPérez, Mariano1 aOrduña, Juan, M1 aTárraga, Joaquín1 aDopazo, Joaquín  uhttp://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1574-301960nas a2200325   4500008004100000022001400041245009200055210006900147260001600216300000800232490000700240520091900247653001801166653002001184653002001204653004201224653001101266653001301277653002801290653002201318100002101340700002301361700002301384700002201407700002401429700002001453700001901473700002001492856012201512       2017                        eng d  a1471-210500aVISMapper: ultra-fast exhaustive cartography of viral insertion sites for gene therapy.0 aVISMapper ultrafast exhaustive cartography of viral insertion si  c2017 Sep 20  a4210 v183 a<p><b>BACKGROUND: </b>The possibility of integrating viral vectors to become a persistent part of the host genome makes them a crucial element of clinical gene therapy. However, viral integration has associated risks, such as the unintentional activation of oncogenes that can result in cancer. Therefore, the analysis of integration sites of retroviral vectors is a crucial step in developing safer vectors for therapeutic use.</p><p><b>RESULTS: </b>Here we present VISMapper, a vector integration site analysis web server, to analyze next-generation sequencing data for retroviral vector integration sites. VISMapper can be found at: http://vismapper.babelomics.org .</p><p><b>CONCLUSIONS: </b>Because it uses novel mapping algorithms VISMapper is remarkably faster than previous available programs. It also provides a useful graphical interface to analyze the integration sites found in the genomic context.</p>10aBase Sequence10aGenetic Therapy10aGenetic Vectors10aHigh-Throughput Nucleotide Sequencing10aHumans10aInternet10aUser-Computer Interface10aVirus Integration1 aJuanes, José, M1 aGallego, Asunción1 aTárraga, Joaquín1 aChaves, Felipe, J1 aMarin-Garcia, Pablo1 aMedina, Ignacio1 aArnau, Vicente1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/vismapper-ultra-fast-exhaustive-cartography-viral-insertion-sites-gene-therapy00587nas a2200157   4500008004100000245007900041210006900120260001600189490000700205100002300212700002300235700001900258700002000277700002000297856011200317       2016                        eng d00aHPG pore: an efficient and scalable framework for nanopore sequencing data0 aHPG pore an efficient and scalable framework for nanopore sequen  cJan-12-20160 v171 aTárraga, Joaquín1 aGallego, Asunción1 aArnau, Vicente1 aMedina, Ignacio1 aDopazo, Joaquin  uhttp://www.biomedcentral.com/1471-2105/17/107http://link.springer.com/content/pdf/10.1186/s12859-016-0966-001901nas a2200241   4500008004100000022001400041245008000055210006900135260000900204300000800213490000700221520123600228653001101464653000801475653001301483653000801496100002301504700002301527700001901550700002001569700002001589856005001609       2016                        eng d  a1471-210500aHPG pore: an efficient and scalable framework for nanopore sequencing data.0 aHPG pore an efficient and scalable framework for nanopore sequen  c2016  a1070 v173 aBACKGROUND: 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 .10ahadoop10aHPC10ananopore10aNGS1 aTárraga, Joaquín1 aGallego, Asunción1 aArnau, Vicente1 aMedina, Ignacio1 aDopazo, Joaquin  uhttp://www.biomedcentral.com/1471-2105/17/10702445nas a2200469   4500008004100000022001400041245008300055210006900138260001600207300001400223490000700237520108700244653001501331653002101346653002201367653001601389653002101405653000801426653001201434653002001446653002001466100002001486700002401506700002901530700003101559700001701590700002401607700002301631700002201654700002401676700002101700700001801721700002201739700001901761700003601780700002301816700002301839700002001862700002001882700002001902856005301922       2015                        eng d  a1362-496200aBabelomics 5.0: functional interpretation for new generations of genomic data.0 aBabelomics 50 functional interpretation for new generations of g  c2015 Apr 20  aW117-W1210 v433 aBabelomics 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.10ababelomics10adata integration10agene set analysis10ainteractome10anetwork analysis10aNGS10aRNA-seq10aSystems biology10atranscriptomics1 aAlonso, Roberto1 aSalavert, Francisco1 aGarcia-Garcia, Francisco1 aCarbonell-Caballero, José1 aBleda, Marta1 aGarcía-Alonso, Luz1 aSanchis-Juan, Alba1 aPerez-Gil, Daniel1 aMarin-Garcia, Pablo1 aSánchez, Rubén1 aCubuk, Cankut1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aHernansaiz-Ballesteros, Rosa, D1 aAlemán, Alejandro1 aTárraga, Joaquín1 aMontaner, David1 aMedina, Ignacio1 aDopazo, Joaquin  uhttp://nar.oxfordjournals.org/content/43/W1/W11701828nas a2200253   4500008004100000022001400041245007200055210006900127260001700196300001300213490000700226520106800233653000801301653000801309653002301317100002101340700002301361700002001384700002301404700002201427700002001449700003001469856007501499       2015                        eng d  a1557-996400aConcurrent and Accurate Short Read Mapping on Multicore Processors.0 aConcurrent and Accurate Short Read Mapping on Multicore Processo  c2015 Sep-Oct  a995-10070 v123 aWe 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.10aHPC10aNGS10ashort real mapping1 aMartinez, Hector1 aTárraga, Joaquín1 aMedina, Ignacio1 aBarrachina, Sergio1 aCastillo, Maribel1 aDopazo, Joaquin1 aQuintana-Orti, Enrique, S  uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=701000502556nas a2200301   4500008004100000022001400041245008500055210006900140260001600209300000700225490000700232520160800239653001501847653001801862653001301880653004201893653001101935653002301946653002701969653001301996100002002009700002002029700002302049700002002072700002002092700002202112856012002134       2015                        eng d  a1471-210500aFast inexact mapping using advanced tree exploration on backward search methods.0 aFast inexact mapping using advanced tree exploration on backward  c2015 Jan 28  a180 v163 a<p><b>BACKGROUND: </b>Short sequence mapping methods for Next Generation Sequencing consist on a combination of seeding techniques followed by local alignment based on dynamic programming approaches. Most seeding algorithms are based on backward search alignment, using the Burrows Wheeler Transform, the Ferragina and Manzini Index or Suffix Arrays. All these backward search algorithms have excellent performance, but their computational cost highly increases when allowing errors. In this paper, we discuss an inexact mapping algorithm based on pruning strategies for search tree exploration over genomic data.</p><p><b>RESULTS: </b>The proposed algorithm achieves a 13x speed-up over similar algorithms when allowing 6 base errors, including insertions, deletions and mismatches. This algorithm can deal with 400 bps reads with up to 9 errors in a high quality Illumina dataset. In this example, the algorithm works as a preprocessor that reduces by 55% the number of reads to be aligned. Depending on the aligner the overall execution time is reduced between 20-40%.</p><p><b>CONCLUSIONS: </b>Although not intended as a complete sequence mapping tool, the proposed algorithm could be used as a preprocessing step to modern sequence mappers. This step significantly reduces the number reads to be aligned, accelerating overall alignment time. Furthermore, this algorithm could be used for accelerating the seeding step of already available sequence mappers. In addition, an out-of-core index has been implemented for working with large genomes on systems without expensive memory configurations.</p>10aAlgorithms10aGenome, Human10aGenomics10aHigh-Throughput Nucleotide Sequencing10aHumans10aSequence Alignment10aSequence Analysis, DNA10aSoftware1 aSalavert, José1 aTomás, Andrés1 aTárraga, Joaquín1 aMedina, Ignacio1 aDopazo, Joaquin1 aBlanquer, Ignacio  uhttps://www.clinbioinfosspa.es/content/fast-inexact-mapping-using-advanced-tree-exploration-backward-search-methods02283nas a2200253   4500008004100000022001400041245009200055210006900147260001600216300001400232490000700246520154200253653001101795653000801806653001601814653000801830100002301838700002001861700002101881700001701902700002001919700002101939856006901960       2015                        eng d  a1367-481100aA Parallel and Sensitive Software Tool for Methylation Analysis on Multicore Platforms.0 aParallel and Sensitive Software Tool for Methylation Analysis on  c2015 Jun 10  a3130-31380 v313 aMOTIVATION: 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 "anonymous"). CONTACT: Juan.Orduna@uv.es.10aBS-seq10aHPC10amethylation10aNGS1 aTárraga, Joaquín1 aPérez, Mariano1 aOrduña, Juan, M1 aDuato, José1 aMedina, Ignacio1 aDopazo, Joaquín  uhttp://bioinformatics.oxfordjournals.org/content/31/19/3130.long01441nas a2200265   4500008004100000022001400041245009700055210006900152260001600221300001400237490000700251520057300258653000800831653004300839100002300882700001900905700002100924700001700945700001900962700002700981700002901008700002101037700002001058856009701078       2014                        eng d  a1367-481100aAcceleration of short and long DNA read mapping without loss of accuracy using suffix array.0 aAcceleration of short and long DNA read mapping without loss of   c2014 Aug 20  a3396-33980 v303 aHPG 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.10aNGS10ashort read mapping. HPC. suffix arrays1 aTárraga, Joaquín1 aArnau, Vicente1 aMartinez, Hector1 aMoreno, Raul1 aCazorla, Diego1 aSalavert-Torres, José1 aBlanquer-Espert, Ignacio1 aDopazo, Joaquín1 aMedina, Ignacio  uhttp://bioinformatics.oxfordjournals.org/content/early/2014/08/19/bioinformatics.btu553.long02076nas a2200241   4500008004100000022001400041245014000055210006900195260001300264300001200277490000700289520129000296100001701586700002301603700002401626700002401650700002401674700001901698700001901717700002001736700002001756856005801776       2012                        eng d  a1362-496200aCellBase, a comprehensive collection of RESTful web services for retrieving relevant biological information from heterogeneous sources.0 aCellBase a comprehensive collection of RESTful web services for   c2012 Jul  aW609-140 v403 aDuring the past years, the advances in high-throughput technologies have produced an unprecedented growth in the number and size of repositories and databases storing relevant biological data. Today, there is more biological information than ever but, unfortunately, the current status of many of these repositories is far from being optimal. Some of the most common problems are that the information is spread out in many small databases; frequently there are different standards among repositories and some databases are no longer supported or they contain too specific and unconnected information. In addition, data size is increasingly becoming an obstacle when accessing or storing biological data. All these issues make very difficult to extract and integrate information from different sources, to analyze experiments or to access and query this information in a programmatic way. CellBase provides a solution to the growing necessity of integration by easing the access to biological data. CellBase implements a set of RESTful web services that query a centralized database containing the most relevant biological data sources. The database is hosted in our servers and is regularly updated. CellBase documentation can be found at http://docs.bioinfo.cipf.es/projects/cellbase.1 aBleda, Marta1 aTárraga, Joaquín1 aDe Maria, Alejandro1 aSalavert, Francisco1 aGarcía-Alonso, Luz1 aCelma, Matilde1 aMartin, Ainoha1 aDopazo, Joaquin1 aMedina, Ignacio  uhttp://nar.oxfordjournals.org/content/40/W1/W609.long02025nas a2200349   4500008004100000022001400041245011700055210006900172260001300241300001100254490000700265520090400272653002501176653001301201653001301214653001401227653002301241653001301264100002101277700002101298700002301319700002001342700002101362700001701383700002401400700003301424700002401457700002001481700002001501700002001521856013401541       2011                        eng d  a1362-496200aPhylemon 2.0: a suite of web-tools for molecular evolution, phylogenetics, phylogenomics and hypotheses testing.0 aPhylemon 20 a suite of webtools for molecular evolution phylogen  c2011 Jul  aW470-40 v393 a<p>Phylemon 2.0 is a new release of the suite of web tools for molecular evolution, phylogenetics, phylogenomics and hypotheses testing. It has been designed as a response to the increasing demand of molecular sequence analyses for experts and non-expert users. Phylemon 2.0 has several unique features that differentiates it from other similar web resources: (i) it offers an integrated environment that enables evolutionary analyses, format conversion, file storage and edition of results; (ii) it suggests further analyses, thereby guiding the users through the web server; and (iii) it allows users to design and save phylogenetic pipelines to be used over multiple genes (phylogenomics). Altogether, Phylemon 2.0 integrates a suite of 30 tools covering sequence alignment reconstruction and trimming; tree reconstruction, visualization and manipulation; and evolutionary hypotheses testing.</p>10aEvolution, Molecular10aGenomics10aInternet10aPhylogeny10aSequence Alignment10aSoftware1 aSánchez, Rubén1 aSerra, François1 aTárraga, Joaquín1 aMedina, Ignacio1 aCarbonell, José1 aPulido, Luis1 aDe Maria, Alejandro1 aCapella-Gutíerrez, Salvador1 aHuerta-Cepas, Jaime1 aGabaldón, Toni1 aDopazo, Joaquin1 aDopazo, Hernán  uhttps://www.clinbioinfosspa.es/content/phylemon-20-suite-web-tools-molecular-evolution-phylogenetics-phylogenomics-and-hypotheses02554nas a2200361   4500008004100000245014100041210006900182260001600251300003100267490000700298520145100305653001501756653002001771653001501791653001001806653000801816653000901824100002001833700002101853700001701874700002101891700001801912700001601930700002301946700002901969700002701998700002002025700002302045700002002068700002002088700002102108856006302129       2010                        eng d00aBabelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling.0 aBabelomics an integrative platform for the analysis of transcrip  c2010 May 16  aW210-W213. Featured in NAR0 v383 a<p>Babelomics is a response to the growing necessity of integrating and analyzing different types of genomic data in an environment that allows an easy functional interpretation of the results. Babelomics includes a complete suite of methods for the analysis of gene expression data that include normalization (covering most commercial platforms), pre-processing, differential gene expression (case-controls, multiclass, survival or continuous values), predictors, clustering; large-scale genotyping assays (case controls and TDTs, and allows population stratification analysis and correction). All these genomic data analysis facilities are integrated and connected to multiple options for the functional interpretation of the experiments. Different methods of functional enrichment or gene set enrichment can be used to understand the functional basis of the experiment analyzed. Many sources of biological information, which include functional (GO, KEGG, Biocarta, Reactome, etc.), regulatory (Transfac, Jaspar, ORegAnno, miRNAs, etc.), text-mining or protein-protein interaction modules can be used for this purpose. Finally a tool for the de novo functional annotation of sequences has been included in the system. This provides support for the functional analysis of non-model species. Mirrors of Babelomics or command line execution of their individual components are now possible. Babelomics is available at http://www.babelomics.org.</p>10ababelomics10agene expression10agenotyping10agepas10aGSA10aGWAS1 aMedina, Ignacio1 aCarbonell, José1 aPulido, Luis1 aMadeira, Sara, C1 aGoetz, Stefan1 aConesa, Ana1 aTárraga, Joaquín1 aPascual-Montano, Alberto1 aNogales-Cadenas, Ruben1 aSantoyo, Javier1 aGarcía, Francisco1 aMarbà, Martina1 aMontaner, David1 aDopazo, Joaquín  uhttp://nar.oxfordjournals.org/content/38/suppl_2/W210.full02028nas a2200349   4500008004100000022001400041245014400055210006900199260001300268300001100281490000700292520085700299653002501156653002101181653001101202653001001213653002201223653003401245653001101279653003601290653001301326653002801339100002001367700002001387700002101407700002601428700002101454700002301475700002501498700002001523856013501543       2009                        eng d  a1362-496200aGene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies.0 aGene setbased analysis of polymorphisms finding pathways or biol  c2009 Jul  aW340-40 v373 a<p>Genome-wide association studies have become a popular strategy to find associations of genes to traits of interest. Despite the high-resolution available today to carry out genotyping studies, the success of its application in real studies has been limited by the testing strategy used. As an alternative to brute force solutions involving the use of very large cohorts, we propose the use of the Gene Set Analysis (GSA), a different analysis strategy based on testing the association of modules of functionally related genes. We show here how the Gene Set-based Analysis of Polymorphisms (GeSBAP), which is a simple implementation of the GSA strategy for the analysis of genome-wide association studies, provides a significant increase in the power testing for this type of studies. GeSBAP is freely available at http://bioinfo.cipf.es/gesbap/.</p>10aBiological Phenomena10aBreast Neoplasms10aFemale10aGenes10aGenetic Variation10aGenome-Wide Association Study10aHumans10aPolymorphism, Single Nucleotide10aSoftware10aUser-Computer Interface1 aMedina, Ignacio1 aMontaner, David1 aBonifaci, Núria1 aPujana, Miguel, Angel1 aCarbonell, José1 aTárraga, Joaquín1 aAl-Shahrour, Fátima1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/gene-set-based-analysis-polymorphisms-finding-pathways-or-biological-processes-associated-001715nas a2200265   4500008004100000245014300041210006900184300001300253490000700266520085600273653001501129653001301144653001101157653002701168653000801195100002001203700002001223700002101243700002601264700002101290700002301311700002401334700002001358856007101378       2009                        eng d00aGene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies0 aGene setbased analysis of polymorphisms finding pathways or biol  aW340-3440 v373 a<p>Genome-wide association studies have become a popular strategy to find associations of genes to traits of interest. Despite the high-resolution available today to carry out genotyping studies, the success of its application in real studies has been limited by the testing strategy used. As an alternative to brute force solutions involving the use of very large cohorts, we propose the use of the Gene Set Analysis (GSA), a different analysis strategy based on testing the association of modules of functionally related genes. We show here how the Gene Set-based Analysis of Polymorphisms (GeSBAP), which is a simple implementation of the GSA strategy for the analysis of genome-wide association studies, provides a significant increase in the power testing for this type of studies. GeSBAP is freely available at http://bioinfo.cipf.es/gesbap/</p>10ababelomics10agene set10aGESBAP10apathway-based analysis10aSNP1 aMedina, Ignacio1 aMontaner, David1 aBonifaci, Núria1 aPujana, Miguel, Angel1 aCarbonell, José1 aTárraga, Joaquín1 aAl-Shahrour, Fatima1 aDopazo, Joaquin  uhttp://nar.oxfordjournals.org/cgi/content/abstract/37/suppl_2/W34002503nas a2200385   4500008004100000022001400041245007700055210006900132260001600201300001200217490000700229520130100236653002201537653003701559653003001596653001301626653001301639653004401652653001301696100002301709700002001732700002101752700002401773700001901797700001601816700002501832700002801857700001801885700001901903700002901922700001601951700002001967700002001987856011002007       2008                        eng d  a1362-496200aGEPAS, a web-based tool for microarray data analysis and interpretation.0 aGEPAS a webbased tool for microarray data analysis and interpret  c2008 Jul 01  aW308-140 v363 a<p>Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org.</p>10aComputer Graphics10aDose-Response Relationship, Drug10aGene Expression Profiling10aInternet10aKinetics10aOligonucleotide Array Sequence Analysis10aSoftware1 aTárraga, Joaquín1 aMedina, Ignacio1 aCarbonell, José1 aHuerta-Cepas, Jaime1 aMinguez, Pablo1 aAlloza, Eva1 aAl-Shahrour, Fátima1 aVegas-Azcárate, Susana1 aGoetz, Stefan1 aEscobar, Pablo1 aGarcia-Garcia, Francisco1 aConesa, Ana1 aMontaner, David1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/gepas-web-based-tool-microarray-data-analysis-and-interpretation-003690nas a2200937   4500008004100000022001400041245007800055210006900133260001300202300001100215490000600226520100100232653002601233653003201259653002301291653003801314653001301352653002601365653002401391110002301415700002301438700001901461700001801480700002501498700001801523700001901541700002101560700001601581700001601597700002901613700001701642700001901659700002201678700002501700700003101725700002501756700001601781700001901797700001601816700002001832700002601852700002501878700001901903700001901922700001801941700001901959700001401978700001901992700002002011700002002031700001702051700002002068700002102088700002402109700002102133700002102154700002102175700002202196700001802218700002002236700002302256700002402279700002502303700002002328700002002348700002002368700002002388700002202408700002002430700002302450700001702473700001602490700002702506700001802533700001802551700001902569700002002588700001802608700002302626856010302649       2008                        eng d  a1477-405400aInteroperability with Moby 1.0--it's better than sharing your toothbrush!0 aInteroperability with Moby 10its better than sharing your toothb  c2008 May  a220-310 v93 a<p>The BioMoby project was initiated in 2001 from within the model organism database community. It aimed to standardize methodologies to facilitate information exchange and access to analytical resources, using a consensus driven approach. Six years later, the BioMoby development community is pleased to announce the release of the 1.0 version of the interoperability framework, registry Application Programming Interface and supporting Perl and Java code-bases. Together, these provide interoperable access to over 1400 bioinformatics resources worldwide through the BioMoby platform, and this number continues to grow. Here we highlight and discuss the features of BioMoby that make it distinct from other Semantic Web Service and interoperability initiatives, and that have been instrumental to its deployment and use by a wide community of bioinformatics service providers. The standard, client software, and supporting code libraries are all freely available at http://www.biomoby.org/.</p>10aComputational Biology10aDatabase Management Systems10aDatabases, Factual10aInformation Storage and Retrieval10aInternet10aProgramming Languages10aSystems Integration1 aBioMoby Consortium1 aWilkinson, Mark, D1 aSenger, Martin1 aKawas, Edward1 aBruskiewich, Richard1 aGouzy, Jerome1 aNoirot, Celine1 aBardou, Philippe1 aNg, Ambrose1 aHaase, Dirk1 aSaiz, Enrique, de Andres1 aWang, Dennis1 aGibbons, Frank1 aGordon, Paul, M K1 aSensen, Christoph, W1 aCarrasco, Jose, Manuel Rod1 aFernández, José, M1 aShen, Lixin1 aLinks, Matthew1 aNg, Michael1 aOpushneva, Nina1 aNeerincx, Pieter, B T1 aLeunissen, Jack, A M1 aErnst, Rebecca1 aTwigger, Simon1 aUsadel, Bjorn1 aGood, Benjamin1 aWong, Yan1 aStein, Lincoln1 aCrosby, William1 aKarlsson, Johan1 aRoyo, Romina1 aPárraga, Iván1 aRamírez, Sergio1 aGelpi, Josep, Lluis1 aTrelles, Oswaldo1 aPisano, David, G1 aJimenez, Natalia1 aKerhornou, Arnaud1 aRosset, Roman1 aZamacola, Leire1 aTárraga, Joaquín1 aHuerta-Cepas, Jaime1 aCarazo, Jose, María1 aDopazo, Joaquin1 aGuigó, Roderic1 aNavarro, Arcadi1 aOrozco, Modesto1 aValencia, Alfonso1 aClaros, Gonzalo1 aPérez, Antonio, J1 aAldana, Jose1 aRojano, Mar1 aCruz, Raul, Fernandez-1 aNavas, Ismael1 aSchiltz, Gary1 aFarmer, Andrew1 aGessler, Damian1 aSchoof, Heiko1 aGroscurth, Andreas  uhttps://www.clinbioinfosspa.es/content/interoperability-moby-10-its-better-sharing-your-toothbrush02680nas a2200385   4500008004100000022001400041245016600055210006900221260001300290300001000303490000700313520140700320653002201727653001201749653001801761653002601779653003001805653001001835653001301845653001101858653001301869653004401882653002601926653001301952653002401965653002601989100002502015700001902040700002302059700002002082700001602102700002002118700002002138856013602158       2007                        eng d  a1362-496200aFatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments.0 aFatiGO a functional profiling tool for genomic data Integration   c2007 Jul  aW91-60 v353 a<p>The ultimate goal of any genome-scale experiment is to provide a functional interpretation of the data, relating the available information with the hypotheses that originated the experiment. Thus, functional profiling methods have become essential in diverse scenarios such as microarray experiments, proteomics, etc. We present the FatiGO+, a web-based tool for the functional profiling of genome-scale experiments, specially oriented to the interpretation of microarray experiments. In addition to different functional annotations (gene ontology, KEGG pathways, Interpro motifs, Swissprot keywords and text-mining based bioentities related to diseases and chemical compounds) FatiGO+ includes, as a novelty, regulatory and structural information. The regulatory information used includes predictions of targets for distinct regulatory elements (obtained from the Transfac and CisRed databases). Additionally FatiGO+ uses predictions of target motifs of miRNA to infer which of these can be activated or deactivated in the sample of genes studied. Finally, properties of gene products related to their relative location and connections in the interactome have also been used. Also, enrichment of any of these functional terms can be directly analysed on chromosomal coordinates. FatiGO+ can be found at: http://www.fatigoplus.org and within the Babelomics environment http://www.babelomics.org.</p>10aAmino Acid Motifs10aAnimals10aBinding Sites10aComputational Biology10aGene Expression Profiling10aGenes10aGenomics10aHumans10aInternet10aOligonucleotide Array Sequence Analysis10aProgramming Languages10aSoftware10aSystems Integration10aTranscription Factors1 aAl-Shahrour, Fátima1 aMinguez, Pablo1 aTárraga, Joaquín1 aMedina, Ignacio1 aAlloza, Eva1 aMontaner, David1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/fatigo-functional-profiling-tool-genomic-data-integration-functional-annotation-regulatory-001771nas a2200205   4500008004100000022001400041245009400055210006900149260001600218300001000234490000600244520105200250100001701302700002001319700002701339700002301366700002501389700002001414856013101434       2007                        eng d  a0973-206300aFunctional profiling and gene expression analysis of chromosomal copy number alterations.0 aFunctional profiling and gene expression analysis of chromosomal  c2007 Apr 10  a432-50 v13 a<p>Contrarily to the traditional view in which only one or a few key genes were supposed to be the causative factors of diseases, we discuss the importance of considering groups of functionally related genes in the study of pathologies characterised by chromosomal copy number alterations. Recent observations have reported the existence of regions in higher eukaryotic chromosomes (including humans) containing genes of related function that show a high degree of coregulation. Copy number alterations will consequently affect to clusters of functionally related genes, which will be the final causative agents of the diseased phenotype, in many cases. Therefore, we propose that the functional profiling of the regions affected by copy number alterations must be an important aspect to take into account in the understanding of this type of pathologies. To illustrate this, we present an integrated study of DNA copy number variations, gene expression along with the functional profiling of chromosomal regions in a case of multiple myeloma.</p>1 aConde, Lucia1 aMontaner, David1 aBurguet-Castell, Jordi1 aTárraga, Joaquín1 aAl-Shahrour, Fátima1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/functional-profiling-and-gene-expression-analysis-chromosomal-copy-number-alterations-002059nas a2200385   4500008004100000022001400041245012300055210006900178260001300247300001000260490000700270520079500277653001201072653002101084653002601105653002201131653003001153653001101183653001301194653002001207653003101227653004401258653002601302653001301328653002401341653002801365100001701393700002001410700002701430700002301457700002001480700002501500700002001525856012801545       2007                        eng d  a1362-496200aISACGH: a web-based environment for the analysis of Array CGH and gene expression which includes functional profiling.0 aISACGH a webbased environment for the analysis of Array CGH and   c2007 Jul  aW81-50 v353 a<p>We present the ISACGH, a web-based system that allows for the combination of genomic data with gene expression values and provides different options for functional profiling of the regions found. Several visualization options offer a convenient representation of the results. Different efficient methods for accurate estimation of genomic copy number from array-CGH hybridization data have been included in the program. Moreover, the connection to the gene expression analysis package GEPAS allows the use of different facilities for data pre-processing and analysis. A DAS server allows exporting the results to the Ensembl viewer where contextual genomic information can be obtained. The program is freely available at: http://isacgh.bioinfo.cipf.es or within http://www.gepas.org.</p>10aAnimals10aCluster Analysis10aComputational Biology10aComputer Graphics10aGene Expression Profiling10aHumans10aInternet10aModels, Genetic10aNucleic Acid Hybridization10aOligonucleotide Array Sequence Analysis10aProgramming Languages10aSoftware10aSystems Integration10aUser-Computer Interface1 aConde, Lucia1 aMontaner, David1 aBurguet-Castell, Jordi1 aTárraga, Joaquín1 aMedina, Ignacio1 aAl-Shahrour, Fátima1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/isacgh-web-based-environment-analysis-array-cgh-and-gene-expression-which-includes-0