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Title: Automated identification of reference genes based on RNA-seq data.
Authors: Carmona, Rosario
Arroyo, Macarena
Jiménez-Quesada, María José
Seoane, Pedro
Zafra, Adoración
Larrosa, Rafael
Alché, Juan de Dios
Claros, M Gonzalo
Keywords: Cancer;Normalization;Olive (Olea europaea L.);Quantitative PCR;Real-time PCR;Reference genes
metadata.dc.subject.mesh: Arabidopsis
Cell Line, Tumor
Gene Expression Profiling
Reference Standards
Sequence Analysis, RNA
Issue Date: 18-Aug-2017
Abstract: Gene expression analyses demand appropriate reference genes (RGs) for normalization, in order to obtain reliable assessments. Ideally, RG expression levels should remain constant in all cells, tissues or experimental conditions under study. Housekeeping genes traditionally fulfilled this requirement, but they have been reported to be less invariant than expected; therefore, RGs should be tested and validated for every particular situation. Microarray data have been used to propose new RGs, but only a limited set of model species and conditions are available; on the contrary, RNA-seq experiments are more and more frequent and constitute a new source of candidate RGs. An automated workflow based on mapped NGS reads has been constructed to obtain highly and invariantly expressed RGs based on a normalized expression in reads per mapped million and the coefficient of variation. This workflow has been tested with Roche/454 reads from reproductive tissues of olive tree (Olea europaea L.), as well as with Illumina paired-end reads from two different accessions of Arabidopsis thaliana and three different human cancers (prostate, small-cell cancer lung and lung adenocarcinoma). Candidate RGs have been proposed for each species and many of them have been previously reported as RGs in literature. Experimental validation of significant RGs in olive tree is provided to support the algorithm. Regardless sequencing technology, number of replicates, and library sizes, when RNA-seq experiments are designed and performed, the same datasets can be analyzed with our workflow to extract suitable RGs for subsequent PCR validation. Moreover, different subset of experimental conditions can provide different suitable RGs.
metadata.dc.identifier.doi: 10.1186/s12938-017-0356-5
Appears in Collections:Producción 2020

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