02565nas a2200253   4500008004100000022001400041245011000055210006900165260001600234300000800250490000700258520169900265653002601964653002301990653001302013653002802026100002202054700002402076700002002100700002402120700002002144700002002164856012702184       2019                        eng d  a1471-210500aPyCellBase, an efficient python package for easy retrieval of biological data from heterogeneous sources.0 aPyCellBase an efficient python package for easy retrieval of bio  c2019 Mar 28  a1590 v203 a<p><b>BACKGROUND: </b>Biological databases and repositories are incrementing in diversity and complexity over the years. This rapid expansion of current and new sources of biological knowledge raises serious problems of data accessibility and integration. To handle the growing necessity of unification, CellBase was created as an integrative solution. CellBase provides a centralized NoSQL database containing biological information from different and heterogeneous sources. Access to this information is done through a RESTful web service API, which provides an efficient interface to the data.</p><p><b>RESULTS: </b>In this work we present PyCellBase, a Python package that provides programmatic access to the rich RESTful web service API offered by CellBase. This package offers a fast and user-friendly access to biological information without the need of installing any local database. In addition, a series of command-line tools are provided to perform common bioinformatic tasks, such as variant annotation. CellBase data is always available by a high-availability cluster and queries have been tuned to ensure a real-time performance.</p><p><b>CONCLUSION: </b>PyCellBase is an open-source Python package that provides an efficient access to heterogeneous biological information. It allows to perform tasks that require a comprehensive set of knowledge resources, as for example variant annotation. Queries can be easily fine-tuned to retrieve the desired information of particular biological features. PyCellBase offers the convenience of an object-oriented scripting language and provides the ability to integrate the obtained results into other Python applications and pipelines.</p>10aComputational Biology10aDatabases, Factual10aSoftware10aUser-Computer Interface1 aPerez-Gil, Daniel1 aLopez, Francisco, J1 aDopazo, Joaquin1 aMarin-Garcia, Pablo1 aRendon, Augusto1 aMedina, Ignacio  uhttp://clinbioinfosspa.es/content/pycellbase-efficient-python-package-easy-retrieval-biological-data-heterogeneous-sources02395nas a2200313   4500008004100000022001400041245011300055210006900168260001600237300000800253490000700261520133500268653001201603653002301615653003001638653004201668653001301710653002701723653001801750653002801768100002101796700002201817700002201839700002101861700002101882700002001903700001901923856013901942       2017                        eng d  a1471-210500aATGC transcriptomics: a web-based application to integrate, explore and analyze de novo transcriptomic data.0 aATGC transcriptomics a webbased application to integrate explore  c2017 Feb 22  a1210 v183 a<p><b>BACKGROUND: </b>In the last years, applications based on massively parallelized RNA sequencing (RNA-seq) have become valuable approaches for studying non-model species, e.g., without a fully sequenced genome. RNA-seq is a useful tool for detecting novel transcripts and genetic variations and for evaluating differential gene expression by digital measurements. The large and complex datasets resulting from functional genomic experiments represent a challenge in data processing, management, and analysis. This problem is especially significant for small research groups working with non-model species.</p><p><b>RESULTS: </b>We developed a web-based application, called ATGC transcriptomics, with a flexible and adaptable interface that allows users to work with new generation sequencing (NGS) transcriptomic analysis results using an ontology-driven database. This new application simplifies data exploration, visualization, and integration for a better comprehension of the results.</p><p><b>CONCLUSIONS: </b>ATGC transcriptomics provides access to non-expert computer users and small research groups to a scalable storage option and simple data integration, including database administration and management. The software is freely available under the terms of GNU public license at http://atgcinta.sourceforge.net .</p>10aAnimals10aDatabases, Genetic10aGene Expression Profiling10aHigh-Throughput Nucleotide Sequencing10aInternet10aSequence Analysis, RNA10aTranscriptome10aUser-Computer Interface1 aGonzalez, Sergio1 aClavijo, Bernardo1 aRivarola, Máximo1 aMoreno, Patricio1 aFernandez, Paula1 aDopazo, Joaquin1 aPaniego, Norma  uhttp://clinbioinfosspa.es/content/atgc-transcriptomics-web-based-application-integrate-explore-and-analyze-de-novo-transcriptomic-data02299nas a2200361   4500008004100000022001400041245004600055210004400101260001600145300001400161490000700175520132500182653002201507653001801529653001101547653001301558653001301571653002801584100001801612700001701630700002101647700002301668700002301691700002201714700001601736700001701752700001901769700001801788700002001806700002001826700002001846856007101866       2017                        eng d  a1362-496200aHGVA: the Human Genome Variation Archive.0 aHGVA the Human Genome Variation Archive  c2017 Jul 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/gkx44501955nas a2200325   4500008004100000022001400041245009200055210006900147260001600216300000800232490000700240520091900247653001801166653002001184653002001204653004201224653001101266653001301277653002801290653002201318100002101340700002301361700002301384700002201407700002401429700002001453700001901473700002001492856011701512       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  uhttp://clinbioinfosspa.es/content/vismapper-ultra-fast-exhaustive-cartography-viral-insertion-sites-gene-therapy02712nas a2200325   4500008004100000022001400041245015600055210006900211260001300280300001000293490000700303520157800310653002801888653002201916653004201938653001301980653003401993653001302027653003602040653001302076653002802089100002002117700002402137700001702161700002402178700002002202700002602222700002002248856011802268       2012                        eng d  a1362-496200aVARIANT: Command Line, Web service and Web interface for fast and accurate functional characterization of variants found by Next-Generation Sequencing.0 aVARIANT Command Line Web service and Web interface for fast and   c2012 Jul  aW54-80 v403 a<p>The massive use of Next-Generation Sequencing (NGS) technologies is uncovering an unexpected amount of variability. The functional characterization of such variability, particularly in the most common form of variation found, the Single Nucleotide Variants (SNVs), has become a priority that needs to be addressed in a systematic way. VARIANT (VARIant ANalyis Tool) reports information on the variants found that include consequence type and annotations taken from different databases and repositories (SNPs and variants from dbSNP and 1000 genomes, and disease-related variants from the Genome-Wide Association Study (GWAS) catalog, Online Mendelian Inheritance in Man (OMIM), Catalog of Somatic Mutations in Cancer (COSMIC) mutations, etc). VARIANT also produces a rich variety of annotations that include information on the regulatory (transcription factor or miRNA-binding sites, etc.) or structural roles, or on the selective pressures on the sites affected by the variation. This information allows extending the conventional reports beyond the coding regions and expands the knowledge on the contribution of non-coding or synonymous variants to the phenotype studied. Contrarily to other tools, VARIANT uses a remote database and operates through efficient RESTful Web Services that optimize search and transaction operations. In this way, local problems of installation, update or disk size limitations are overcome without the need of sacrifice speed (thousands of variants are processed per minute). VARIANT is available at: http://variant.bioinfo.cipf.es.</p>10aDatabases, Nucleic Acid10aGenetic Variation10aHigh-Throughput Nucleotide Sequencing10aInternet10aMolecular Sequence Annotation10amutation10aPolymorphism, Single Nucleotide10aSoftware10aUser-Computer Interface1 aMedina, Ignacio1 aDe Maria, Alejandro1 aBleda, Marta1 aSalavert, Francisco1 aAlonso, Roberto1 aGonzalez, Cristina, Y1 aDopazo, Joaquin  uhttp://clinbioinfosspa.es/content/variant-command-line-web-service-and-web-interface-fast-and-accurate-functional02481nas a2200277   4500008004100000022001400041245007400055210006900129260000900198300001100207490000600218520162800224653002201852653002301874653001301897653001301910653003401923653002801957100002301985700002402008700001602032700002002048700001902068700001902087856009702106       2011                        eng d  a1932-620300amyKaryoView: a light-weight client for visualization of genomic data.0 amyKaryoView a lightweight client for visualization of genomic da  c2011  ae263450 v63 a<p>The Distributed Annotation System (DAS) is a protocol for easy sharing and integration of biological annotations. In order to visualize feature annotations in a genomic context a client is required. Here we present myKaryoView, a simple light-weight DAS tool for visualization of genomic annotation. myKaryoView has been specifically configured to help analyse data derived from personal genomics, although it can also be used as a generic genome browser visualization. Several well-known data sources are provided to facilitate comparison of known genes and normal variation regions. The navigation experience is enhanced by simultaneous rendering of different levels of detail across chromosomes. A simple interface is provided to allow searches for any SNP, gene or chromosomal region. User-defined DAS data sources may also be added when querying the system. We demonstrate myKaryoView capabilities for adding user-defined sources with a set of genetic profiles of family-related individuals downloaded directly from 23andMe. myKaryoView is a web tool for visualization of genomic data specifically designed for direct-to-consumer genomic data that uses publicly available data distributed throughout the Internet. It does not require data to be held locally and it is capable of rendering any feature as long as it conforms to DAS specifications. Configuration and addition of sources to myKaryoView can be done through the interface. Here we show a proof of principle of myKaryoView's ability to display personal genomics data with 23andMe genome data sources. The tool is available at: http://mykaryoview.com.</p>10aComputer Graphics10aDatabases, Genetic10aGenomics10aInternet10aMolecular Sequence Annotation10aUser-Computer Interface1 aJimenez, Rafael, C1 aSalazar, Gustavo, A1 aGel, Bernat1 aDopazo, Joaquin1 aMulder, Nicola1 aCorpas, Manuel  uhttp://clinbioinfosspa.es/content/mykaryoview-light-weight-client-visualization-genomic-data02023nas a2200349   4500008004100000022001400041245014400055210006900199260001300268300001100281490000700292520085700299653002501156653002101181653001101202653001001213653002201223653003401245653001101279653003601290653001301326653002801339100002001367700002001387700002101407700002601428700002101454700002301475700002501498700002001523856013001543       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  uhttp://clinbioinfosspa.es/content/gene-set-based-analysis-polymorphisms-finding-pathways-or-biological-processes-associated-002514nas a2200337   4500008004100000022001400041245007300055210006900128260001600197300000800213490000600221520148900227653001501716653002301731653002401754653003001778653002301808653002101831653002401852653001301876653002001889653002801909100002501937700002101962700002001983700002402003700001902027700002002046700002002066856009002086       2007                        eng d  a1471-210500aFrom genes to functional classes in the study of biological systems.0 aFrom genes to functional classes in the study of biological syst  c2007 Apr 03  a1140 v83 a<p><b>BACKGROUND: </b>With the popularization of high-throughput techniques, the need for procedures that help in the biological interpretation of results has increased enormously. Recently, new procedures inspired in systems biology criteria have started to be developed.</p><p><b>RESULTS: </b>Here we present FatiScan, a web-based program which implements a threshold-independent test for the functional interpretation of large-scale experiments that does not depend on the pre-selection of genes based on the multiple application of independent tests to each gene. The test implemented aims to directly test the behaviour of blocks of functionally related genes, instead of focusing on single genes. In addition, the test does not depend on the type of the data used for obtaining significance values, and consequently different types of biologically informative terms (gene ontology, pathways, functional motifs, transcription factor binding sites or regulatory sites from CisRed) can be applied to different classes of genome-scale studies. We exemplify its application in microarray gene expression, evolution and interactomics.</p><p><b>CONCLUSION: </b>Methods for gene set enrichment which, in addition, are independent from the original data and experimental design constitute a promising alternative for the functional profiling of genome-scale experiments. A web server that performs the test described and other similar ones can be found at: http://www.babelomics.org.</p>10aAlgorithms10aChromosome Mapping10aComputer Simulation10aGene Expression Profiling10aModels, Biological10aMultigene Family10aSignal Transduction10aSoftware10aSystems biology10aUser-Computer Interface1 aAl-Shahrour, Fátima1 aArbiza, Leonardo1 aDopazo, Hernán1 aHuerta-Cepas, Jaime1 aMinguez, Pablo1 aMontaner, David1 aDopazo, Joaquin  uhttp://clinbioinfosspa.es/content/genes-functional-classes-study-biological-systems-002054nas a2200385   4500008004100000022001400041245012300055210006900178260001300247300001000260490000700270520079500277653001201072653002101084653002601105653002201131653003001153653001101183653001301194653002001207653003101227653004401258653002601302653001301328653002401341653002801365100001701393700002001410700002701430700002301457700002001480700002501500700002001525856012301545       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  uhttp://clinbioinfosspa.es/content/isacgh-web-based-environment-analysis-array-cgh-and-gene-expression-which-includes-0