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ATGC transcriptomics : a web-based application to integrate, explore and analyze de novo transcriptomic data
Resumen
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...]
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 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.
[Cerrar]

Autor
Gonzalez, Sergio Alberto;
Clavijo, Bernardo;
Rivarola, Maximo Lisandro;
Moreno, Patricio;
Fernández, Paula;
Dopazo, Joaquín;
Paniego, Norma Beatriz;
Fuente
BMC bioinformatics 18 (121) : 1-9. (2017)
Fecha
2017-02-22
Formato
pdf
Tipo de documento
artículo
Palabras Claves
Derechos de acceso
Abierto
