This study compares the transcriptome profiling capabilities of RNA-Seq and microarray technologies using human T cell activation samples. The authors found that while both methods showed high correlation in gene expression profiles, RNA-Seq outperformed microarrays in detecting low-abundance transcripts, differentiating biologically critical isoforms, and identifying genetic variants. RNA-Seq also demonstrated a broader dynamic range, allowing for the detection of more differentially expressed genes with higher fold-change. The study highlights the advantages of RNA-Seq in avoiding technical issues inherent to microarray probe performance, such as cross-hybridization and limited detection range. Despite these benefits, RNA-Seq is currently more expensive and less widely used due to its novelty and complex data analysis requirements. The authors expect that as these barriers are overcome, RNA-Seq will become the predominant tool for transcriptome analysis.This study compares the transcriptome profiling capabilities of RNA-Seq and microarray technologies using human T cell activation samples. The authors found that while both methods showed high correlation in gene expression profiles, RNA-Seq outperformed microarrays in detecting low-abundance transcripts, differentiating biologically critical isoforms, and identifying genetic variants. RNA-Seq also demonstrated a broader dynamic range, allowing for the detection of more differentially expressed genes with higher fold-change. The study highlights the advantages of RNA-Seq in avoiding technical issues inherent to microarray probe performance, such as cross-hybridization and limited detection range. Despite these benefits, RNA-Seq is currently more expensive and less widely used due to its novelty and complex data analysis requirements. The authors expect that as these barriers are overcome, RNA-Seq will become the predominant tool for transcriptome analysis.