RNA-seq reveals more consistent reference genes for gene expression studies in human non-melanoma skin cancers
1
Dermatology Research Center, School of Medicine, Translational Research Institute, Princess Alexandra Hospital, The University of Queensland, Brisbane, Queensland, Australia
2
Garvan Institute of Medical Research, Sydney, New South Wales, Australia
3
St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
4
Wellman Centre for Photomedicine, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, United Stated of America
5
Diamantina Institute, University of Queensland, Brisbane, Outside U.S./Canada, Australia
6
Diamantina Institute, Translational Research Institute, Princess Alexandra Hospital, The University of Queensland, Brisbane, Queensland, Australia
- Published
- Accepted
- Subject Areas
- Genomics, Molecular Biology, Dermatology, Oncology, Medical Genetics
- Keywords
- RNA-seq, reference gene, qPCR, non-melanoma skin cancer
- Copyright
- © 2016 Hoang et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. RNA-seq reveals more consistent reference genes for gene expression studies in human non-melanoma skin cancers. PeerJ Preprints 4:e2331v1 https://doi.org/10.7287/peerj.preprints.2331v1
Abstract
Identification of appropriate reference genes (RGs) is critical to accurate data interpretation in quantitative real-time PCR (qPCR) experiments. In this study, we have utilised next generation RNA-sequencing (RNA-seq) to analyse the transcriptome of a panel of non-melanoma skin cancer lesions, identifying genes, which are consistently expressed across all samples. Genes encoding ribosomal proteins were amongst the most stable in this dataset. Validation of this RNA-seq data was examined using qPCR to confirm the suitability of a set of highly stable genes for use as RGs. These genes will provide a valuable resource for the normalisation of qPCR data for the analysis of non-melanoma skin cancer.
Author Comment
This is a submission to PeerJ for review.