RNA sequencing (RNA-Seq) technology enables rapid profiling and deep investigation of the transcriptome, for any species. This approach offers a number of advantages compared to microarray analysis, a legacy technology often used in gene expression studies.
Ability to detect novel transcripts: Unlike arrays, RNA-Seq technology does not require species- or transcript-specific probes. It can detect novel transcripts, gene fusions, single nucleotide variants, indels (small insertions and deletions), and other previously unknown changes that arrays cannot detect.1,2
Wider dynamic range: With array hybridization technology, gene expression measurement is limited by background at the low end and signal saturation at the high end. RNA-Seq technology produces discrete, digital sequencing read counts, and can quantify expression across a larger dynamic range (>105 for RNA-Seq vs. 103 for arrays).1,2,3
Higher specificity and sensitivity: Compared to microarrays, RNA-Seq technology can detect a higher percentage of differentially expressed genes, especially genes with low expression.4-6
Simple detection of rare and low-abundance transcripts: Sequencing coverage depth can easily be increased to detect rare transcripts, single transcripts per cell, or weakly expressed genes.
“mRNA-Seq offers improved specificity, so it’s better at detecting transcripts, and specifically isoforms, than microarrays. It’s also more sensitive in detecting differential expression and offers increased dynamic range.”
In the past, next-generation sequencing (NGS) data analysis required extensive bioinformatics expertise, presenting a major hurdle to adoption of RNA sequencing technology by biologists. The latest user-friendly tools vastly simplify the analysis process, providing accessible solutions for researchers without a bioinformatics background.
The portion of NIH grant funding allocated to new RNA sequencing vs. gene expression microarray-inclusive grants has been trending towards RNA-Seq technology for the last several years, and now constitutes the majority. Download our transcriptomics eBook to see the evidence.