RNA-Seq and Microarray Technology Comparison
RNA sequencing vs microarrays
RNA-Seq technology generates an unbiased view of the transcriptome, and offers a number of advantages compared to microarrays
Benefits of RNA-Seq vs microarrays
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.
How to start your first RNA-Seq project
Many researchers start their NGS journey with RNA sequencing, which provides insight into gene expression levels across sample types. This video provides a high-level overview of the RNA sequencing workflow.
"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."
Illumina total RNA-Seq workflow video
This animated video takes you through the Illumina total RNA sequencing workflow and covers the advantages of RNA-Seq over other RNA analysis methods, such as gene expression microarrays and qPCR. Discover why RNA-Seq is a powerful sequencing method for visibility into previously undetectable changes in gene expression.
More reasons to switch from arrays to RNA-Seq
Additional advantages of transitioning to NGS-based RNA sequencing include the development of intuitive RNA-Seq data analysis tools designed for biologists as well as grant funding trends.
User-friendly data analysis tools
In the past, NGS data analysis required extensive bioinformatics expertise, presenting a hurdle to adoption of RNA sequencing technology by biologists. The latest user-friendly software tools vastly simplify the analysis process, providing accessible solutions for researchers without a bioinformatics background to adopt RNA-Seq instead of gene expression arrays.
RNA-Seq grants outpace array grants
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. Publication trends demonstrate the rapid adoption and increasing impact of RNA-Seq–based research.
RNA sequencing video series
Introduction to Illumina RNA library prep workflows
This video covers key considerations for experiment planning and library preparation kit options for RNA input.
Best practices for Illumina RNA prep protocols
In the second part of our RNA-Seq video webinar series, we dive into best practices for Illumina RNA library prep kits.
Introduction to RNA-Seq analysis
In this video, we discuss the user-friendly RNA-Seq analysis options available on BaseSpace Sequence Hub, a data analysis and management hub.
Additional resources
New to NGS eBook
For an in-depth look at NGS compared to to other methods, download our Getting Started with NGS eBook. This eBook outlines key differences, recommended methods/applications, sample workflows, and more.
Gene expression and regulation informatics eBook
This eBook outlines the NGS data analysis workflow and provides an overview of Illumina software solutions for analyzing gene expression and regulation data.
Getting started with RNA-Seq
RNA sequencing, the characterization of coding and noncoding regions of the transcriptome, is an accessible entry point for using NGS, given its versatility across use cases and well-established workflows.
Related content
Benchtop sequencers
Benchtop sequencers are a popular way of getting started with NGS. This resource is a great place to learn about benchtop sequencing capabilities, instruments, and popular applications and methods for each.
Dynamic cancer transcriptome exposes new therapeutic targets
Learn how sequencing RNA as well as DNA is giving cancer researchers an extra dimension of information.
RNA library preparation
Find simple, customized RNA-Seq library prep kits and workflows for your experiment.
Have questions about bringing NGS into your lab?
If you have questions about NGS for your specific research focus, we’d love to help. Our specialists can answer any questions and recommend the best solution for your setup.
References
- Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10:57–63.
- Wilhelm BT, Landry JR. RNA-Seq—quantitative measurement of expression through massively parallel RNA sequencing. Methods. 2009;48:249–57.
- 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.
- 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.
- 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.
- Liu Y, Morley M, Brandimarto J, et al. RNA-Seq identifies novel myocardial gene expression signatures of heart failure. Genomics. 2015;105:83–89.