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dc.contributor.authorMontoto Louzao, Julián
dc.contributor.authorGómez-Carballa, Alberto
dc.contributor.authorBello, Xabier
dc.contributor.authorPardo-Seco, Jacobo
dc.contributor.authorCamino-Mera, Alba
dc.contributor.authorViz-Lasheras, Sandra
dc.contributor.authorMartín, María J.
dc.contributor.authorMartinón-Torres, Federico
dc.contributor.authorSalas, Antonio
dc.date.accessioned2024-09-09T11:24:39Z
dc.date.available2024-09-09T11:24:39Z
dc.date.issued2024-08
dc.identifier.citationJulián Montoto-Louzao, Alberto Gómez-Carballa, Xabier Bello, Jacobo Pardo-Seco, Alba Camino-Mera, Sandra Viz-Lasheras, María J Martín, Federico Martinón-Torres, Antonio Salas, GUANIN: an all-in-one GUi-driven analyzer for NanoString interactive normalization, Bioinformatics, Volume 40, Issue 8, August 2024, btae462, https://doi.org/10.1093/bioinformatics/btae462es_ES
dc.identifier.urihttp://hdl.handle.net/2183/38913
dc.descriptionGUANIN can be installed with pip install GUANIN and it is available at https://pypi.org/project/guanin/. Source code, documentation, and case studies are available at https://github.com/julimontoto/guanin under the GPLv3 license.es_ES
dc.description.abstract[Abstract]: Most tools for normalizing NanoString gene expression data, apart from the default NanoString nCounter software, are R packages that focus on technical normalization and lack configurable parameters. However, content normalization is the most sensitive, experiment-specific, and relevant step to preprocess NanoString data. Currently this step requires the use of multiple tools and a deep understanding of data management by the researcher. We present GUANIN, a comprehensive normalization tool that integrates both new and well-established methods, offering a wide variety of options to introduce, filter, choose, and evaluate reference genes for content normalization. GUANIN allows the introduction of genes from an endogenous subset as reference genes, addressing housekeeping-related selection problems. It performs a specific and straightforward normalization approach for each experiment, using a wide variety of parameters with suggested default values. GUANIN provides a large number of informative output files that enable the iterative refinement of the normalization process. In terms of normalization, GUANIN matches or outperforms other available methods. Importantly, it allows researchers to interact comprehensively with the data preprocessing step without programming knowledge, thanks to its easy-to-use Graphical User Interface (GUI).es_ES
dc.description.sponsorshipThe supercomputer FinisTerrae III and its permanent data storage system have been funded by the Spanish Ministry of Science and Innovation, the Galician Government, and the European Regional Development Fund (ERDF). This study also received support by (i) ISCIII: TRINEO: PI22/00162; DIAVIR: DTS19/00049; Resvi-Omics: PI19/01039 (A.S.), ReSVinext: PI16/01569, Enterogen: PI19/01090 (F.M.-T.), cofinanciados FEDER, (ii) GAIN: IN607B 2020/08 and IN607A 2023/02 (A.S.), GEN-COVID: IN845D 2020/23 (F.M.-T.), IIN607A2021/05 (F.M.-T.); (iii) ACIS: BI-BACVIR (PRIS-3, to A.S.), CovidPhy (SA 304 C, to A.S.); and (iv) consorcio Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CB21/06/00103; to A.S. and F.M.-T.). In addition, this study has been funded by ISCIII through the project “CP23/00080” and co-funded by the European Union. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.es_ES
dc.description.sponsorshipXunta de Galicia; IN607B 2020/08es_ES
dc.description.sponsorshipXunta de Galicia; IN607A 2023/02es_ES
dc.description.sponsorshipXunta de Galicia; IN845D 2020/23es_ES
dc.description.sponsorshipXunta de Galicia; IIN607A2021/05es_ES
dc.description.sponsorshipCentro de Investigación Biomédica en Red de Enfermedades Respiratorias; CB21/06/00103es_ES
dc.language.isoenges_ES
dc.publisherOxford University Presses_ES
dc.relationinfo:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PI22%2F00162/ES/TrINeo: Aproximación ‘ómica’ al entrenamiento de la inmunidad innata del recién nacido a través de la vacunación de la madrees_ES
dc.relationinfo:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DTS19%2F00049/ES/DIAVIR: DESARROLLO Y VALIDACION CLINICA DE UNA HERRAMIENTA MOLECULAR PARA EL DIAGNOSTICO DE ENFERMEDADES VIRALES EN SANGRE Y SALIVA BASADO EN LA DETECCION DE BIOMARCADORES TRANSCRIPTOMICOSes_ES
dc.relationinfo:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PI19%2F01039/ES/RESVI-OMICS: HACIA UNA MEDICINA PERSONALIZADA EN LA INFECCION POR EL VIRUS RESPIRATORIO SINCITIAL A TRAVES DEL USO DE UNA APROXIMACION ‘-OMICA’es_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/PI16%2F01569/ES/ReSVinext: Estudio integrado y traslacional de la infección por Virus Respiratorio Sincitiales_ES
dc.relationinfo:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PI19%2F01090/ES/ABORDAJE INTERACTOMICO TRASLACIONAL DE LA INFECCION POR ENTEROVIRUS (PROJECTO ENTEROGEN)es_ES
dc.relationinfo:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/CP23%2F00080/ES/es_ES
dc.relation.urihttps://doi.org/10.1093/bioinformatics/btae462es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectNanoStringes_ES
dc.subjectGUANINes_ES
dc.subjectGene Expression Profilinges_ES
dc.subjectHumanses_ES
dc.subjectSoftwarees_ES
dc.subjectUser-Computer Interfacees_ES
dc.titleGUANIN: an all-in-one GUi-driven analyzer for NanoString interactive normalizationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleBioinformaticses_ES
UDC.volume40es_ES
UDC.issue8es_ES
UDC.startPagebtae462es_ES
dc.identifier.doi10.1093/bioinformatics/btae462


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