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Abstract
Background: 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 [ver mas...]
dc.contributor.authorGonzalez, Sergio Alberto
dc.contributor.authorClavijo, Bernardo
dc.contributor.authorRivarola, Maximo Lisandro
dc.contributor.authorMoreno, Patricio
dc.contributor.authorFernández, Paula
dc.contributor.authorDopazo, Joaquín
dc.contributor.authorPaniego, Norma Beatriz
dc.date.accessioned2017-06-28T15:35:40Z
dc.date.available2017-06-28T15:35:40Z
dc.date.issued2017-02-22
dc.identifier.urihttp://hdl.handle.net/20.500.12123/502
dc.identifier.urihttps://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1494-2
dc.description.abstractBackground: 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.
dc.formatapplication/pdf
dc.language.isoeng
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourceBMC bioinformatics 18 (121) : 1-9. (2017)
dc.subjectProgramas de ordenador
dc.subjectWorld Wide Web
dc.subjectSecuencia de ARN
dc.titleATGC transcriptomics : a web-based application to integrate, explore and analyze de novo transcriptomic data
dc.typeinfo:ar-repo/semantics/artículo
dc.typeinfo:eu-repo/semantics/articleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.description.origenInst. de Biotecnología
dc.gic152935
dc.description.filFil: Gonzalez, Sergio Alberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina
dc.description.filFil: Clavijo, Bernardo. Norwich Research Park. Earlham Institute; Reino Unido
dc.description.filFil: Rivarola, Maximo Lisandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.filFil: Moreno, Patricio. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina
dc.description.filFil: Fernández, Paula. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina
dc.description.filFil: Dopazo, Joaquín Centro de Investigación Príncipe Felipe. Computational Genomics Department; España
dc.description.filFil: Paniego, Norma Beatriz. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.subtypecientifico


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