Advantages of RNA-Seq technology

Wider dynamic range and higher sensitivity than microarrays, with novel transcript detection

RNA-Seq and Microarray Technology Comparison

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.

RNA-Seq Technology Overview

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.

Transitioning from Arrays to RNA-Seq

Access a detailed comparison of microarray and RNA-Seq technologies, from the perspective of a sequencing service provider.

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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.

Explore RNA-Seq Data Analysis Tools

Benchtop RNA-Seq Technology

The NextSeq 550 System provides the flexible power you need for transcriptome, whole-genome, and targeted sequencing.

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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.

Empowering Transcriptomics

This eBook discusses how NGS is advancing gene expression research, and includes a section on the advantages of RNA-Seq over arrays.

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RNA Sequencing Considerations

Each RNA-Seq experiment type—whether it’s gene expression profiling, targeted RNA expression, or small RNA analysis—has unique requirements for read length and depth. This bulletin reviews experimental considerations and offers resources to help with study design.

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RNA Sequencing Considerations

Explore a wide variety of RNA-Seq methods, from mRNA-Seq to specialized methods for analyzing RNA from cancer samples and more.

See All RNA-Seq Methods

Single-Cell RNA Sequencing

Providing a high-resolution view of cell-to-cell variation.

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References
  1. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10:57–63.
  2. Wilhelm BT, Landry JR. RNA-Seq—quantitative measurement of expression through massively parallel RNA sequencing. Methods. 2009;48:249–57.
  3. Zhao S, Fung-Leung WP, Bittner A, and Ngo K, Liu X. Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells. PLoS One. 2014;16;9(1):e78644.
  4. Wang C, Gong B, Bushel PR, et al. The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat Biotechnol. 2014;32:926–932.
  5. Li J, Hou R, Niu X, et al. Comparison of microarray and RNA-Seq analysis of mRNA expression in dermal mesenchymal stem cells. Biotechnol Lett. 2016;38:33–41.
  6. Liu Y, Morley M, Brandimarto J, et al. RNA-Seq identifies novel myocardial gene expression signatures of heart failure. Genomics. 2015;105:83–89.