03508nas a2200709 4500008004100000022001400041245008200055210006900137260001500206300001600221490000700237520139500244653001201639653002301651653001801674653002301692653001001715653001901725653002201744653002501766653001801791653001301809653001101822653001301833653002301846653001301869653001001882100002401892700001801916700002601934700002501960700002101985700002102006700002602027700002002053700002902073700002302102700002902125700002602154700002002180700002702200700002702227700001802254700001902272700003002291700001802321700003402339700001902373700002902392700002702421700001902448700002502467700001702492700002802509700002802537700001902565700002202584700001902606700002002625710004302645856011002688 2021 eng d a1362-496200aCSVS, a crowdsourcing database of the Spanish population genetic variability.0 aCSVS a crowdsourcing database of the Spanish population genetic c2021 01 08 aD1130-D11370 v493 a
The knowledge of the genetic variability of the local population is of utmost importance in personalized medicine and has been revealed as a critical factor for the discovery of new disease variants. Here, we present the Collaborative Spanish Variability Server (CSVS), which currently contains more than 2000 genomes and exomes of unrelated Spanish individuals. This database has been generated in a collaborative crowdsourcing effort collecting sequencing data produced by local genomic projects and for other purposes. Sequences have been grouped by ICD10 upper categories. A web interface allows querying the database removing one or more ICD10 categories. In this way, aggregated counts of allele frequencies of the pseudo-control Spanish population can be obtained for diseases belonging to the category removed. Interestingly, in addition to pseudo-control studies, some population studies can be made, as, for example, prevalence of pharmacogenomic variants, etc. In addition, this genomic data has been used to define the first Spanish Genome Reference Panel (SGRP1.0) for imputation. This is the first local repository of variability entirely produced by a crowdsourcing effort and constitutes an example for future initiatives to characterize local variability worldwide. CSVS is also part of the GA4GH Beacon network. CSVS can be accessed at: http://csvs.babelomics.org/.
10aAlleles10aChromosome Mapping10aCrowdsourcing10aDatabases, Genetic10aExome10aGene Frequency10aGenetic Variation10aGenetics, Population10aGenome, Human10aGenomics10aHumans10aInternet10aPrecision Medicine10aSoftware10aSpain1 aPeña-Chilet, Maria1 aRoldán, Gema1 aPerez-Florido, Javier1 aOrtuno, Francisco, M1 aCarmona, Rosario1 aAquino, Virginia1 aLópez-López, Daniel1 aLoucera, Carlos1 aFernandez-Rueda, Jose, L1 aGallego, Asunción1 aGarcia-Garcia, Francisco1 aGonzález-Neira, Anna1 aPita, Guillermo1 aNúñez-Torres, Rocío1 aSantoyo-López, Javier1 aAyuso, Carmen1 aMinguez, Pablo1 aAvila-Fernandez, Almudena1 aCorton, Marta1 aMoreno-Pelayo, Miguel, Ángel1 aMorin, Matías1 aGallego-Martinez, Alvaro1 aLopez-Escamez, Jose, A1 aBorrego, Salud1 aAntiňolo, Guillermo1 aAmigo, Jorge1 aSalgado-Garrido, Josefa1 aPasalodos-Sanchez, Sara1 aMorte, Beatriz1 aCarracedo, Ángel1 aAlonso, Ángel1 aDopazo, Joaquin1 aSpanish Exome Crowdsourcing Consortium uhttps://www.clinbioinfosspa.es/content/csvs-crowdsourcing-database-spanish-population-genetic-variability02824nas a2200397 4500008004100000022001400041245011600055210006900171260000900240300000600249490000600255520157600261653002601837653002401863653001901887653002901906653001101935653001301946653003601959653002301995653001402018653001402032653001302046653001802059100001802077700002202095700001902117700001602136700002402152700002202176700002102198700002002219700003102239700002002270856013602290 2019 eng d a2056-718900aDifferential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models.0 aDifferential metabolic activity and discovery of therapeutic tar c2019 a70 v53 aIn spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling. We present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway metabolic module activities that can also be used for class prediction and in silico prediction of knock-out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer. Metabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions.
10aComputational Biology10aComputer Simulation10aDrug discovery10aGene Regulatory Networks10aHumans10aInternet10aMetabolic Networks and Pathways10aModels, Biological10aNeoplasms10aPhenotype10aSoftware10aTranscriptome1 aCubuk, Cankut1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aRian, Kinza1 aSalavert, Francisco1 aPujana, Miguel, A1 aMateo, Francesca1 aHerranz, Carmen1 aCarbonell-Caballero, José1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/differential-metabolic-activity-and-discovery-therapeutic-targets-using-summarized-metabolic02380nas a2200313 4500008004100000022001400041245011300055210006900168260001600237300000800253490000700261520133500268653001201603653002301615653003001638653004201668653001301710653002701723653001801750653002801768100002101796700002201817700002201839700002101861700002101882700002001903700001901923856012401942 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 aBACKGROUND: 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.
RESULTS: 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.
CONCLUSIONS: 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 .
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 uhttps://www.clinbioinfosspa.es/content/atgc-transcriptomics-web-based-application-integrate-explore-and-analyze-de-novo02298nas 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 aHigh-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.
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/gkx44501960nas 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 aBACKGROUND: 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.
RESULTS: 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 .
CONCLUSIONS: 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.
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-therapy01578nas a2200253 4500008004100000022001400041245006500055210006300120260001500183300001100198490000700209520080700216653002601023653001301049653001301062100002401075700002401099700002101123700002001144700001701164700002001181700002001201856010301221 2016 eng d a1367-481100aWeb-based network analysis and visualization using CellMaps.0 aWebbased network analysis and visualization using CellMaps c2016 10 01 a3041-30 v323 aUNLABELLED: : CellMaps is an HTML5 open-source web tool that allows displaying, editing, exploring and analyzing biological networks as well as integrating metadata into them. Computations and analyses are remotely executed in high-end servers, and all the functionalities are available through RESTful web services. CellMaps can easily be integrated in any web page by using an available JavaScript API.
AVAILABILITY AND IMPLEMENTATION: The application is available at: http://cellmaps.babelomics.org/ and the code can be found in: https://github.com/opencb/cell-maps The client is implemented in JavaScript and the server in C and Java.
CONTACT: jdopazo@cipf.es
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
10aBiochemical Phenomena10aInternet10aSoftware1 aSalavert, Francisco1 aGarcía-Alonso, Luz1 aSánchez, Rubén1 aAlonso, Roberto1 aBleda, Marta1 aMedina, Ignacio1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/web-based-network-analysis-and-visualization-using-cellmaps02330nas a2200265 4500008004100000022001400041245012000055210006900175260001300244300001300257490000700270520140800277653002301685653001301708653003201721653004301753100001901796700001901815700001601834700002401850700002001874700002401894700001501918856013101933 2015 eng d a1362-496200aPTMcode v2: a resource for functional associations of post-translational modifications within and between proteins.0 aPTMcode v2 a resource for functional associations of posttransla c2015 Jan aD494-5020 v433 aThe post-translational regulation of proteins is mainly driven by two molecular events, their modification by several types of moieties and their interaction with other proteins. These two processes are interdependent and together are responsible for the function of the protein in a particular cell state. Several databases focus on the prediction and compilation of protein-protein interactions (PPIs) and no less on the collection and analysis of protein post-translational modifications (PTMs), however, there are no resources that concentrate on describing the regulatory role of PTMs in PPIs. We developed several methods based on residue co-evolution and proximity to predict the functional associations of pairs of PTMs that we apply to modifications in the same protein and between two interacting proteins. In order to make data available for understudied organisms, PTMcode v2 (http://ptmcode.embl.de) includes a new strategy to propagate PTMs from validated modified sites through orthologous proteins. The second release of PTMcode covers 19 eukaryotic species from which we collected more than 300,000 experimentally verified PTMs (>1,300,000 propagated) of 69 types extracting the post-translational regulation of >100,000 proteins and >100,000 interactions. In total, we report 8 million associations of PTMs regulating single proteins and over 9.4 million interplays tuning PPIs.
10aDatabases, Protein10aInternet10aProtein Interaction Mapping10aProtein Processing, Post-Translational1 aMinguez, Pablo1 aLetunic, Ivica1 aParca, Luca1 aGarcía-Alonso, Luz1 aDopazo, Joaquin1 aHuerta-Cepas, Jaime1 aBork, Peer uhttps://www.clinbioinfosspa.es/content/ptmcode-v2-resource-functional-associations-post-translational-modifications-within-and02231nas a2200313 4500008004100000022001400041245008600055210006900141260001300210300001100223490000700234520127400241653001201515653001101527653001301538653000901551653002401560653002801584653002401612653001301636653001801649100003001667700002101697700002401718700002101742700002001763700002001783856011401803 2013 eng d a1362-496200aInferring the functional effect of gene expression changes in signaling pathways.0 aInferring the functional effect of gene expression changes in si c2013 Jul aW213-70 v413 aSignaling pathways constitute a valuable source of information that allows interpreting the way in which alterations in gene activities affect to particular cell functionalities. There are web tools available that allow viewing and editing pathways, as well as representing experimental data on them. However, few methods aimed to identify the signaling circuits, within a pathway, associated to the biological problem studied exist and none of them provide a convenient graphical web interface. We present PATHiWAYS, a web-based signaling pathway visualization system that infers changes in signaling that affect cell functionality from the measurements of gene expression values in typical expression microarray case-control experiments. A simple probabilistic model of the pathway is used to estimate the probabilities for signal transmission from any receptor to any final effector molecule (taking into account the pathway topology) using for this the individual probabilities of gene product presence/absence inferred from gene expression values. Significant changes in these probabilities allow linking different cell functionalities triggered by the pathway to the biological problem studied. PATHiWAYS is available at: http://pathiways.babelomics.org/.
10aAnimals10aHumans10aInternet10aMice10aModels, Statistical10aReceptors, Cell Surface10aSignal Transduction10aSoftware10aTranscriptome1 aSebastián-Leon, Patricia1 aCarbonell, José1 aSalavert, Francisco1 aSánchez, Rubén1 aMedina, Ignacio1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/inferring-functional-effect-gene-expression-changes-signaling-pathways02001nas a2200313 4500008004100000022001400041245007400055210006900129260001300198300001200211490000700223520105300230653001801283653002301301653001901324653002901343653001301372653001401385653001301399653002601412653001801438100001701456700002001473700002001493700002401513700002401537700002001561856010601581 2012 eng d a1362-496200aInferring the regulatory network behind a gene expression experiment.0 aInferring the regulatory network behind a gene expression experi c2012 Jul aW168-720 v403 aTranscription factors (TFs) and miRNAs are the most important dynamic regulators in the control of gene expression in multicellular organisms. These regulatory elements play crucial roles in development, cell cycling and cell signaling, and they have also been associated with many diseases. The Regulatory Network Analysis Tool (RENATO) web server makes the exploration of regulatory networks easy, enabling a better understanding of functional modularity and network integrity under specific perturbations. RENATO is suitable for the analysis of the result of expression profiling experiments. The program analyses lists of genes and search for the regulators compatible with its activation or deactivation. Tests of single enrichment or gene set enrichment allow the selection of the subset of TFs or miRNAs significantly involved in the regulation of the query genes. RENATO also offers an interactive advanced graphical interface that allows exploring the regulatory network found.RENATO is available at: http://renato.bioinfo.cipf.es/.
10aBinding Sites10aDatabases, Genetic10aFanconi Anemia10aGene Regulatory Networks10aInternet10aMicroRNAs10aSoftware10aTranscription Factors10aTranscriptome1 aBleda, Marta1 aMedina, Ignacio1 aAlonso, Roberto1 aDe Maria, Alejandro1 aSalavert, Francisco1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/inferring-regulatory-network-behind-gene-expression-experiment02236nas a2200325 4500008004100000022001400041245010200055210006900157260001300226300001100239490000700250520119100257653002301448653001101471653001301482653002701495653001401522653003601536653002501572653001301597100002001610700002101630700001801651700002801669700001801697700002001715700002301735700002301758856012901781 2012 eng d a1362-496200aSNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants.0 aSNPeffect 40 online prediction of molecular and structural effec c2012 Jan aD935-90 v403 aSingle nucleotide variants (SNVs) are, together with copy number variation, the primary source of variation in the human genome and are associated with phenotypic variation such as altered response to drug treatment and susceptibility to disease. Linking structural effects of non-synonymous SNVs to functional outcomes is a major issue in structural bioinformatics. The SNPeffect database (http://snpeffect.switchlab.org) uses sequence- and structure-based bioinformatics tools to predict the effect of protein-coding SNVs on the structural phenotype of proteins. It integrates aggregation prediction (TANGO), amyloid prediction (WALTZ), chaperone-binding prediction (LIMBO) and protein stability analysis (FoldX) for structural phenotyping. Additionally, SNPeffect holds information on affected catalytic sites and a number of post-translational modifications. The database contains all known human protein variants from UniProt, but users can now also submit custom protein variants for a SNPeffect analysis, including automated structure modeling. The new meta-analysis application allows plotting correlations between phenotypic features for a user-selected set of variants.
10aDatabases, Protein10aHumans10aInternet10aMeta-Analysis as Topic10aPhenotype10aPolymorphism, Single Nucleotide10aProtein Conformation10aProteins1 aDe Baets, Greet1 aVan Durme, Joost1 aReumers, Joke1 aMaurer-Stroh, Sebastian1 aVanhee, Peter1 aDopazo, Joaquin1 aSchymkowitz, Joost1 aRousseau, Frederic uhttps://www.clinbioinfosspa.es/content/snpeffect-40-line-prediction-molecular-and-structural-effects-protein-coding-variants02717nas a2200325 4500008004100000022001400041245015600055210006900211260001300280300001000293490000700303520157800310653002801888653002201916653004201938653001301980653003401993653001302027653003602040653001302076653002802089100002002117700002402137700001702161700002402178700002002202700002602222700002002248856012302268 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 aThe 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.
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 uhttps://www.clinbioinfosspa.es/content/variant-command-line-web-service-and-web-interface-fast-and-accurate-functional02486nas a2200277 4500008004100000022001400041245007400055210006900129260000900198300001100207490000600218520162800224653002201852653002301874653001301897653001301910653003401923653002801957100002301985700002402008700001602032700002002048700001902068700001902087856010202106 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 aThe 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.
10aComputer Graphics10aDatabases, Genetic10aGenomics10aInternet10aMolecular Sequence Annotation10aUser-Computer Interface1 aJimenez, Rafael, C1 aSalazar, Gustavo, A1 aGel, Bernat1 aDopazo, Joaquin1 aMulder, Nicola1 aCorpas, Manuel uhttps://www.clinbioinfosspa.es/content/mykaryoview-light-weight-client-visualization-genomic-data02025nas 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 aPhylemon 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.
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-hypotheses01786nas a2200277 4500008004100000022001400041245009200055210006900147260001300216300001200229490000700241520089700248653001501145653003001160653001301190653001301203653001801216653004401234653001301278100002401291700002101315700002001336700002001356700001601376856011601392 2010 eng d a1362-496200aSerial Expression Analysis: a web tool for the analysis of serial gene expression data.0 aSerial Expression Analysis a web tool for the analysis of serial c2010 Jul aW239-450 v383 aSerial transcriptomics experiments investigate the dynamics of gene expression changes associated with a quantitative variable such as time or dosage. The statistical analysis of these data implies the study of global and gene-specific expression trends, the identification of significant serial changes, the comparison of expression profiles and the assessment of transcriptional changes in terms of cellular processes. We have created the SEA (Serial Expression Analysis) suite to provide a complete web-based resource for the analysis of serial transcriptomics data. SEA offers five different algorithms based on univariate, multivariate and functional profiling strategies framed within a user-friendly interface and a project-oriented architecture to facilitate the analysis of serial gene expression data sets from different perspectives. SEA is available at sea.bioinfo.cipf.es.
10aAlgorithms10aGene Expression Profiling10aInternet10aKinetics10aLinear Models10aOligonucleotide Array Sequence Analysis10aSoftware1 aNueda, Maria, José1 aCarbonell, José1 aMedina, Ignacio1 aDopazo, Joaquin1 aConesa, Ana uhttps://www.clinbioinfosspa.es/content/serial-expression-analysis-web-tool-analysis-serial-gene-expression-data01737nas a2200277 4500008004100000022001400041245009700055210006900152260001300221300001200234490000700246520083000253653002201083653003701105653002301142653001101165653001301176653003201189653001301221100001901234700001801253700002001271700002501291700002001316856012301336 2009 eng d a1362-496200aSNOW, a web-based tool for the statistical analysis of protein-protein interaction networks.0 aSNOW a webbased tool for the statistical analysis of proteinprot c2009 Jul aW109-140 v373 aUnderstanding the structure and the dynamics of the complex intercellular network of interactions that contributes to the structure and function of a living cell is one of the main challenges of today's biology. SNOW inputs a collection of protein (or gene) identifiers and, by using the interactome as scaffold, draws the connections among them, calculates several relevant network parameters and, as a novelty among the rest of tools of its class, it estimates their statistical significance. The parameters calculated for each node are: connectivity, betweenness and clustering coefficient. It also calculates the number of components, number of bicomponents and articulation points. An interactive network viewer is also available to explore the resulting network. SNOW is available at http://snow.bioinfo.cipf.es.
10aComputer Graphics10aData Interpretation, Statistical10aDatabases, Protein10aHumans10aInternet10aProtein Interaction Mapping10aSoftware1 aMinguez, Pablo1 aGötz, Stefan1 aMontaner, David1 aAl-Shahrour, Fátima1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/snow-web-based-tool-statistical-analysis-protein-protein-interaction-networks-002503nas 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 aGene 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.
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 aThe 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/.
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-toothbrush02961nas a2200409 4500008004100000022001400041245013400055210006900189260001300258300001100271490000700282520167500289653002801964653001201992653002302004653002902027653003002056653001102086653001302097653000902110653001402119653001302133653003602146653001302182653000902195653002102204653002602225100001802251700001702269700002002286700002802306700002102334700002002355700002302375700002302398856013002421 2008 eng d a1362-496200aJoint annotation of coding and non-coding single nucleotide polymorphisms and mutations in the SNPeffect and PupaSuite databases.0 aJoint annotation of coding and noncoding single nucleotide polym c2008 Jan aD825-90 v363 aSingle nucleotide polymorphisms (SNPs) are, together with copy number variation, the primary source of variation in the human genome. SNPs are associated with altered response to drug treatment, susceptibility to disease and other phenotypic variation. Furthermore, during genetic screens for disease-associated mutations in groups of patients and control individuals, the distinction between disease causing mutation and polymorphism is often unclear. Annotation of the functional and structural implications of single nucleotide changes thus provides valuable information to interpret and guide experiments. The SNPeffect and PupaSuite databases are now synchronized to deliver annotations for both non-coding and coding SNP, as well as annotations for the SwissProt set of human disease mutations. In addition, SNPeffect now contains predictions of Tango2: an improved aggregation detector, and Waltz: a novel predictor of amyloid-forming sequences, as well as improved predictors for regions that are recognized by the Hsp70 family of chaperones. The new PupaSuite version incorporates predictions for SNPs in silencers and miRNAs including their targets, as well as additional methods for predicting SNPs in TFBSs and splice sites. Also predictions for mouse and rat genomes have been added. In addition, a PupaSuite web service has been developed to enable data access, programmatically. The combined database holds annotations for 4,965,073 regulatory as well as 133,505 coding human SNPs and 14,935 disease mutations, and phenotypic descriptions of 43,797 human proteins and is accessible via http://snpeffect.vib.be and http://pupasuite.bioinfo.cipf.es/.
10aAmino Acid Substitution10aAnimals10aDatabases, Genetic10aGenetic Diseases, Inborn10aHSP70 Heat-Shock Proteins10aHumans10aInternet10aMice10aMicroRNAs10amutation10aPolymorphism, Single Nucleotide10aProteins10aRats10aRNA Splice Sites10aTranscription Factors1 aReumers, Joke1 aConde, Lucia1 aMedina, Ignacio1 aMaurer-Stroh, Sebastian1 aVan Durme, Joost1 aDopazo, Joaquin1 aRousseau, Frederic1 aSchymkowitz, Joost uhttps://www.clinbioinfosspa.es/content/joint-annotation-coding-and-non-coding-single-nucleotide-polymorphisms-and-mutations-002301nas a2200421 4500008004100000022001400041245005300055210005100108260001300159300001100172490000700183520104700190653001501237653002401252653002601276653003701302653002301339653001301362653002801375653002501403653001301428653002701441653002301468653003101491653003401522653001301556653003601569100002501605700001901630700002201649700001801671700002101689700001901710700002501729700002001754700001701774856008801791 2007 eng d a1362-496200aDBAli tools: mining the protein structure space.0 aDBAli tools mining the protein structure space c2007 Jul aW393-70 v353 aThe DBAli tools use a comprehensive set of structural alignments in the DBAli database to leverage the structural information deposited in the Protein Data Bank (PDB). These tools include (i) the DBAlit program that allows users to input the 3D coordinates of a protein structure for comparison by MAMMOTH against all chains in the PDB; (ii) the AnnoLite and AnnoLyze programs that annotate a target structure based on its stored relationships to other structures; (iii) the ModClus program that clusters structures by sequence and structure similarities; (iv) the ModDom program that identifies domains as recurrent structural fragments and (v) an implementation of the COMPARER method in the SALIGN command in MODELLER that creates a multiple structure alignment for a set of related protein structures. Thus, the DBAli tools, which are freely accessible via the World Wide Web at http://salilab.org/DBAli/, allow users to mine the protein structure space by establishing relationships between protein structures and their functions.
10aAlgorithms10aAmino Acid Sequence10aComputational Biology10aData Interpretation, Statistical10aDatabases, Protein10aInternet10aMolecular Sequence Data10aProtein Conformation10aProteins10aPseudomonas aeruginosa10aSequence Alignment10aSequence Analysis, Protein10aSequence Homology, Amino Acid10aSoftware10aStructure-Activity Relationship1 aMarti-Renom, Marc, A1 aPieper, Ursula1 aMadhusudhan, M, S1 aRossi, Andrea1 aEswar, Narayanan1 aDavis, Fred, P1 aAl-Shahrour, Fátima1 aDopazo, Joaquin1 aSali, Andrej uhttps://www.clinbioinfosspa.es/content/dbali-tools-mining-protein-structure-space-002680nas 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 aThe 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.
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-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 aWe 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.
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