Automated identification of reference genes based on RNA-seq data
Abstract Background 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 fulfil...
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| Format: | Article |
| Language: | English |
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BMC
2017-08-01
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| Series: | BioMedical Engineering OnLine |
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| Online Access: | http://link.springer.com/article/10.1186/s12938-017-0356-5 |
| _version_ | 1818345407181225984 |
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| author | Rosario Carmona Macarena Arroyo María José Jiménez-Quesada Pedro Seoane Adoración Zafra Rafael Larrosa Juan de Dios Alché M. Gonzalo Claros |
| author_facet | Rosario Carmona Macarena Arroyo María José Jiménez-Quesada Pedro Seoane Adoración Zafra Rafael Larrosa Juan de Dios Alché M. Gonzalo Claros |
| author_sort | Rosario Carmona |
| collection | DOAJ |
| description | Abstract Background 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. Results 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. Conclusion 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. |
| first_indexed | 2024-12-13T17:01:53Z |
| format | Article |
| id | doaj.art-156bb23fb86748c7883f42d36b9423bd |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-12-13T17:01:53Z |
| publishDate | 2017-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-156bb23fb86748c7883f42d36b9423bd2022-12-21T23:37:46ZengBMCBioMedical Engineering OnLine1475-925X2017-08-0116S112310.1186/s12938-017-0356-5Automated identification of reference genes based on RNA-seq dataRosario Carmona0Macarena Arroyo1María José Jiménez-Quesada2Pedro Seoane3Adoración Zafra4Rafael Larrosa5Juan de Dios Alché6M. Gonzalo Claros7Plant Reproductive Biology Laboratory, Department of Biochemistry, Cell and Molecular Biology of Plants, Estación Experimental del Zaidín, CSICServicio de Neumología, Hospital Regional Universitario de MálagaPlant Reproductive Biology Laboratory, Department of Biochemistry, Cell and Molecular Biology of Plants, Estación Experimental del Zaidín, CSICDepartamento de Biología Molecular y Bioquímica, Universidad de MálagaPlant Reproductive Biology Laboratory, Department of Biochemistry, Cell and Molecular Biology of Plants, Estación Experimental del Zaidín, CSICDepartamento de Arquitectura de Computadores, Universidad de MálagaPlant Reproductive Biology Laboratory, Department of Biochemistry, Cell and Molecular Biology of Plants, Estación Experimental del Zaidín, CSICDepartamento de Biología Molecular y Bioquímica, Universidad de MálagaAbstract Background 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. Results 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. Conclusion 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.http://link.springer.com/article/10.1186/s12938-017-0356-5Reference genesNormalizationReal-time PCRQuantitative PCROlive (Olea europaea L.)Cancer |
| spellingShingle | Rosario Carmona Macarena Arroyo María José Jiménez-Quesada Pedro Seoane Adoración Zafra Rafael Larrosa Juan de Dios Alché M. Gonzalo Claros Automated identification of reference genes based on RNA-seq data BioMedical Engineering OnLine Reference genes Normalization Real-time PCR Quantitative PCR Olive (Olea europaea L.) Cancer |
| title | Automated identification of reference genes based on RNA-seq data |
| title_full | Automated identification of reference genes based on RNA-seq data |
| title_fullStr | Automated identification of reference genes based on RNA-seq data |
| title_full_unstemmed | Automated identification of reference genes based on RNA-seq data |
| title_short | Automated identification of reference genes based on RNA-seq data |
| title_sort | automated identification of reference genes based on rna seq data |
| topic | Reference genes Normalization Real-time PCR Quantitative PCR Olive (Olea europaea L.) Cancer |
| url | http://link.springer.com/article/10.1186/s12938-017-0356-5 |
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