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

By Shanrong Zhao, W. Fung-Leung, A. Bittner, K. Ngo, Xuejun Liu. PLoS ONE, 2014

976 citations recorded by the source corpus.

To demonstrate the benefits of RNA-Seq over microarray in transcriptome profiling, both RNA-Seq and microarray analyses were performed on RNA samples from a human T cell activation experiment. In contrast to other reports, our analyses focused on the difference, rather than similarity, between RNA-Seq and microarray technologies in transcriptome profiling. A comparison of data sets derived from RNA-Seq and Affymetrix platforms using the same set of samples showed a high correlation between gene expression profiles generated by the two platforms. However, it also demonstrated that RNA-Seq was superior in detecting low abundance transcripts, differentiating biologically critical isoforms, and allowing the identification of genetic variants. RNA-Seq also demonstrated a broader dynamic range than microarray, which allowed for the detection of more differentially expressed genes with higher fold-change. Analysis of the two datasets also showed the benefit derived from avoidance of technical issues inherent to microarray probe performance such as cross-hybridization, non-specific hybridization and limited detection range of individual probes. Because RNA-Seq does not rely on a pre-designed complement sequence detection probe, it is devoid of issues associated with probe redundancy and annotation, which simplified interpretation of the data. Despite the superior benefits of RNA-Seq, microarrays are still the more common choice of researchers when conducting transcriptional profiling experiments. This is likely because RNA-Seq sequencing technology is new to most researchers, more expensive than microarray, data storage is more challenging and analysis is more complex. We expect that once these barriers are overcome, the RNA-Seq platform will become the predominant tool for transcriptome analysis.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8b81af7d7eab9dd6c6bafaa9856028c83cd447eb

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Citations in the corpus

Insights from 20 years of bacterial genome sequencing (2015, methodology): “RNA sequencing (Wang et al. 2014; Westermann et al. 2012; Zhao et al. 2014), which has some substantial advantages, although the statistical analysis packages for this data”
Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols (2020, methodology): “In recent years, RNA-seq has emerged as a powerful technology for transcriptome profiling (Mortazavi et al. 2008; Zhao et al. 2014, 2015).”
A benchmark of gene expression tissue-specificity metrics (2015, background): “It has been reported that the detection of lowly expressed genes is better with RNA-seq then with microarrays (Zhao et al. 2014; Wang et al. 2014; Emig et al. 2011).”
Transcriptomics technologies (2017, neutral): “Comparison of contemporary methods [23] [24] [10].”
The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest (2022, background): “RNA-Seq datasets have increased sensitivity for genes exhibiting low levels of expression (69,70).”