Comparison of RNA-Seq and Microarray in Transcriptome Profiling of Activated T Cells

Comparison of RNA-Seq and Microarray in Transcriptome Profiling of Activated T Cells

January 15, 2014 | Shanrong Zhao, Wai-Ping Fung-Leung, Anton Bittner, Karen Ngo, Xuejun Liu
This study compares RNA-Seq and microarray technologies in transcriptome profiling of activated T cells. Both methods were applied to RNA samples from a human T cell activation experiment. The results show a high correlation between gene expression profiles generated by RNA-Seq and microarray platforms. However, RNA-Seq outperforms microarray in detecting low abundance transcripts, differentiating biologically critical isoforms, and identifying genetic variants. RNA-Seq also has a broader dynamic range, allowing for the detection of more differentially expressed genes with higher fold-change. RNA-Seq avoids technical issues inherent to microarray probe performance, such as cross-hybridization and limited detection range. Despite these advantages, microarrays remain the more common choice due to the novelty, higher cost, and complexity of RNA-Seq. The study concludes that RNA-Seq will become the predominant tool for transcriptome analysis once these barriers are overcome. The data from this study are available in public databases.This study compares RNA-Seq and microarray technologies in transcriptome profiling of activated T cells. Both methods were applied to RNA samples from a human T cell activation experiment. The results show a high correlation between gene expression profiles generated by RNA-Seq and microarray platforms. However, RNA-Seq outperforms microarray in detecting low abundance transcripts, differentiating biologically critical isoforms, and identifying genetic variants. RNA-Seq also has a broader dynamic range, allowing for the detection of more differentially expressed genes with higher fold-change. RNA-Seq avoids technical issues inherent to microarray probe performance, such as cross-hybridization and limited detection range. Despite these advantages, microarrays remain the more common choice due to the novelty, higher cost, and complexity of RNA-Seq. The study concludes that RNA-Seq will become the predominant tool for transcriptome analysis once these barriers are overcome. The data from this study are available in public databases.
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