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dc.contributor.authorBarrero-Rodríguez, Rafael
dc.contributor.authorRodriguez, Jose Manuel 
dc.contributor.authorTarifa, Rocío
dc.contributor.authorVazquez, Jesus 
dc.contributor.authorMastrangelo, Annalaura 
dc.contributor.authorFerrarini, Alessia 
dc.date.accessioned2023-01-19T12:02:30Z
dc.date.available2023-01-19T12:02:30Z
dc.date.issued2022
dc.identifier.citationFront Mol Biosci. 2022 Sep 8; 9:952149es_ES
dc.identifier.issn2296-889Xes_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/15428
dc.description.abstractUntargeted metabolomics aims at measuring the entire set of metabolites in a wide range of biological samples. However, due to the high chemical diversity of metabolites that range from small to large and more complex molecules (i.e., amino acids/carbohydrates vs. phospholipids/gangliosides), the identification and characterization of the metabolome remain a major bottleneck. The first step of this process consists of searching the experimental monoisotopic mass against databases, thus resulting in a highly redundant/complex list of candidates. Despite the progress in this area, researchers are still forced to manually explore the resulting table in order to prioritize the most likely identifications for further biological interpretation or confirmation with standards. Here, we present TurboPutative (https://proteomics.cnic.es/TurboPutative/), a flexible and user-friendly web-based platform composed of four modules (Tagger, REname, RowMerger, and TPMetrics) that streamlines data handling, classification, and interpretability of untargeted LC-MS-based metabolomics data. Tagger classifies the different compounds and provides preliminary insights into the biological system studied. REname improves putative annotation handling and visualization, allowing the recognition of isomers and equivalent compounds and redundant data removal. RowMerger reduces the dataset size, facilitating the manual comparison among annotations. Finally, TPMetrics combines different datasets with feature intensity and relevant information for the researcher and calculates a score based on adduct probability and feature correlations, facilitating further identification, assessment, and interpretation of the results. The TurboPutative web application allows researchers in the metabolomics field that are dealing with massive datasets containing multiple putative annotations to reduce the number of these entries by 80%-90%, thus facilitating the extrapolation of biological knowledge and improving metabolite prioritization for subsequent pathway analysis. TurboPutative comprises a rapid, automated, and customizable workflow that can also be included in programmed bioinformatics pipelines through its RESTful API services. Users can explore the performance of each module through demo datasets supplied on the website. The platform will help the metabolomics community to speed up the arduous task of manual data curation that is required in the first steps of metabolite identification, improving the generation of biological knowledge.es_ES
dc.description.sponsorshipThis research has been possible thanks to a Training of Research Staff (FPU) contract to carry out doctoral theses, granted to RB-R by the Ministry of Universities of Spain [FPU20/03365] and added to project [CSO2014-57826-P]. This study was also supported by competitive grants from the Spanish Ministry of Science, Innovation and Universities (BIO2015-67580-P and PGC2018-097019-B-I00), through the Carlos III Institute of Health-Fondo de Investigación Sanitaria grant PRB3 (IPT17/0019-ISCIII-SGEFI/ERDF, ProteoRed), the Fundació MaratóTV3 (grant 122/C/2015), “la Caixa” Banking Foundation (project code HR17-00247), and the European Research Council (ERC-2016- Consolidator Grant 725091). The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the Ministerio de Ciencia e Innovación (MCIN) and the Pro CNIC Foundation), and is a Severo Ochoa Center of Excellence (grant CEX2020-001041-S funded by MICIN/AEI/10.13039/ 501100011033).es_ES
dc.language.isoenges_ES
dc.publisherFrontiers Media es_ES
dc.type.hasVersionVoRes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleTurboPutative: A web server for data handling and metabolite classification in untargeted metabolomics.es_ES
dc.typejournal articlees_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.identifier.pubmedID36158581es_ES
dc.format.volume9es_ES
dc.format.page952149es_ES
dc.identifier.doi10.3389/fmolb.2022.952149es_ES
dc.contributor.funderMinisterio de Universidades (España) es_ES
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España) es_ES
dc.contributor.funderInstituto de Salud Carlos III es_ES
dc.contributor.funderFundación La Marató TV3 es_ES
dc.contributor.funderFundación La Caixa es_ES
dc.contributor.funderUnión Europea. Comisión Europea. European Research Council (ERC) es_ES
dc.contributor.funderFundación ProCNIC es_ES
dc.description.peerreviewedes_ES
dc.relation.publisherversion10.3389/fmolb.2022.952149es_ES
dc.identifier.journalFrontiers in molecular bioscienceses_ES
dc.repisalud.orgCNICCNIC::Grupos de investigación::Proteómica cardiovasculares_ES
dc.repisalud.institucionCNICes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/HR17-00247es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/ERC-2016-ConsolidatorGrant/725091es_ES
dc.rights.accessRightsopen accesses_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/FPU20/03365es_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/CSO2014-57826-Pes_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/BIO2015-67580-Pes_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/PGC2018-097019-B-I00es_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/IPT17/0019-ISCIII-SGEFI/ERDFes_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/122/C/2015es_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/CEX2020-001041-Ses_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/MICIN/AEI/10.13039/501100011033es_ES


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