RT Journal Article
T1 Automated identification of reference genes based on RNA-seq data.
A1 Carmona, Rosario
A1 Arroyo, Macarena
A1 Jimenez-Quesada, Maria Jose
A1 Seoane, Pedro
A1 Zafra, Adoracion
A1 Larrosa, Rafael
A1 Alche, Juan de Dios
A1 Claros, M Gonzalo
K1 Cancer
K1 Normalization
K1 Olive (Olea europaea L.)
K1 Quantitative PCR
K1 Real-time PCR
K1 Reference genes
AB 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.
PB BioMed Central
YR 2017
FD 2017-08-18
LK http://hdl.handle.net/10668/11527
UL http://hdl.handle.net/10668/11527
LA en
NO Carmona R, Arroyo M, Jiménez-Quesada MJ, Seoane P, Zafra A, Larrosa R, et al.  Automated identification of reference genes based on RNA-seq data. Biomed Eng Online. 2017 Aug 18;16(Suppl 1):65
NO This research was supported by co‑funding from the European Union through the ERDF 2014–2020 “Programa Operativo de Crecimiento Inteligente” to the projects RTA2013‑00068‑C03 and RTA2013‑00023‑C02 of the Spanish INIA; BFU2011-22779 and RECUPERA2020‑3.1.4 from the Spanish MINECO, P11‑CVI‑7487 from the regional PAI, and NEUMOSUR grant 12/2015 entitled “Expresión de retrotransposones en pacientes con adenocarcinoma intervenido. Comparación entre tejido sano y tumoral.” Publication costs were funded by the mentioned grants.
DS RISalud
RD Apr 12, 2025