作者
Charles Wang, Binsheng Gong, Pierre R Bushel, Jean Thierry-Mieg, Danielle Thierry-Mieg, Joshua Xu, Hong Fang, Huixiao Hong, Jie Shen, Zhenqiang Su, Joe Meehan, Xiaojin Li, Lu Yang, Haiqing Li, Paweł P Łabaj, David P Kreil, Dalila Megherbi, Stan Gaj, Florian Caiment, Joost Van Delft, Jos Kleinjans, Andreas Scherer, Viswanath Devanarayan, Jian Wang, Yong Yang, Hui-Rong Qian, Lee J Lancashire, Marina Bessarabova, Yuri Nikolsky, Cesare Furlanello, Marco Chierici, Davide Albanese, Giuseppe Jurman, Samantha Riccadonna, Michele Filosi, Roberto Visintainer, Ke K Zhang, Jianying Li, Jui-Hua Hsieh, Daniel L Svoboda, James C Fuscoe, Youping Deng, Leming Shi, Richard S Paules, Scott S Auerbach, Weida Tong
发表日期
2014/9
期刊
Nature biotechnology
卷号
32
期号
9
页码范围
926-932
出版商
Nature Publishing Group US
简介
The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R2≈0.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance …
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