RNA-seq facilitates unbiased genome-wide gene-expression profiling. However, its concordance with the well-established microarray platform must be rigorously assessed for confident uses in clinical and regulatory application. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same set of liver samples of rats under varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOA). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is highly correlated with treatment effect size, gene-expression abundance and the biological complexity of the MOA. RNA-seq outperforms microarray (90% versus 76%) in DEG verification by quantitative PCR and the main gain is its improved accuracy for low expressed genes. Nonetheless, predictive classifiers derived from both platforms performed similarly. Therefore, the endpoint studied and its biological complexity, transcript abundance, and intended application are important factors in transcriptomic research and for decision-making

Wang, C.; Gong, B.; Bushel, P.R.; Thierry Mieg, J.; Thierry Mieg, D.; Xu, J.; Fang, H.; Hong, H.; Shen, J.; Su, Z.; Meehan, J.; Li, X.; Yang, L.; Li, H.; Łabaj, P.P.; Kreil, D.P.; Megherbi, D.; Gaj, S.; Caiment, F.; van Delft, J.; Kleinjans, J.; Scherer, A.; Devanarayan, V.; Wang, J.; Yang, Y.; Qian, H.R.; Lancashire, L.J.; Bessarabova, M.; Nikolsky, Y.; Furlanello, C.; Chierici, M.; Albanese, D.; Jurman, G.; Riccadonna, S.; Filosi, M.; Visintainer, R.; Zhang, K.K.; Li, J.; Hsieh, J.H.; Svoboda, D.L.; Fuscoe, J.C.; Deng, Y.; Shi, L.; Paules, R.S.; Auerbach, S.S.; Tong, W. (2014). The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. NATURE BIOTECHNOLOGY, 32 (9): 926-932. doi: 10.1038/nbt.3001 handle: http://hdl.handle.net/10449/24750

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

Albanese, Davide;Riccadonna, Samantha;
2014-01-01

Abstract

RNA-seq facilitates unbiased genome-wide gene-expression profiling. However, its concordance with the well-established microarray platform must be rigorously assessed for confident uses in clinical and regulatory application. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same set of liver samples of rats under varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOA). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is highly correlated with treatment effect size, gene-expression abundance and the biological complexity of the MOA. RNA-seq outperforms microarray (90% versus 76%) in DEG verification by quantitative PCR and the main gain is its improved accuracy for low expressed genes. Nonetheless, predictive classifiers derived from both platforms performed similarly. Therefore, the endpoint studied and its biological complexity, transcript abundance, and intended application are important factors in transcriptomic research and for decision-making
Settore BIO/18 - GENETICA
2014
Wang, C.; Gong, B.; Bushel, P.R.; Thierry Mieg, J.; Thierry Mieg, D.; Xu, J.; Fang, H.; Hong, H.; Shen, J.; Su, Z.; Meehan, J.; Li, X.; Yang, L.; Li, H.; Łabaj, P.P.; Kreil, D.P.; Megherbi, D.; Gaj, S.; Caiment, F.; van Delft, J.; Kleinjans, J.; Scherer, A.; Devanarayan, V.; Wang, J.; Yang, Y.; Qian, H.R.; Lancashire, L.J.; Bessarabova, M.; Nikolsky, Y.; Furlanello, C.; Chierici, M.; Albanese, D.; Jurman, G.; Riccadonna, S.; Filosi, M.; Visintainer, R.; Zhang, K.K.; Li, J.; Hsieh, J.H.; Svoboda, D.L.; Fuscoe, J.C.; Deng, Y.; Shi, L.; Paules, R.S.; Auerbach, S.S.; Tong, W. (2014). The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. NATURE BIOTECHNOLOGY, 32 (9): 926-932. doi: 10.1038/nbt.3001 handle: http://hdl.handle.net/10449/24750
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