RNA sequencing (RNA-seq): What it is, how it works, and when to use it

1. Introduction

RNA sequencing (RNA-seq) is a high-throughput technology used to determine RNA sequences from any type of sample and quantify the relative abundance of unique RNA sequences in a population. Unlike older microarray-based methods, RNA-seq provides a comprehensive and unbiased view of the transcriptome, revealing key insights into e.g gene expression, mRNA splicing, non-coding RNA regulation, and enhancer RNA activity. Nearly 100 distinct RNA-seq methods have emerged, driven by advancements in experimental and computational approaches ¹.

RNA-seq is used in disease research, diagnostics, and medication development. It improves public health surveillance and the detection of diseases such as HIV and SARS-CoV-2 by tracking real-time viral strains2. In Mendelian illnesses, RNA-seq plus genomics boosts the diagnosis rates by 15% as it finds causal variations and provides functional molecular evidence. In oncology research, it contributes to discovering biomarkers, analyzing tumor heterogeneity, and resolving drug resistance and immunotherapy challenges. Moreover, its ability to profile dynamic transcriptomes enables the development of tailored medicines with fewer side effects ³.

With all the different techniques available, it can be difficult to know which technique is applicable for which use. The sections below walk through the main RNA-seq methods, their use cases, and how to select the right approach for your study.

2. What is RNA sequencing?

At its core, RNA sequencing converts RNA molecules from a biological sample into a sequencing library, sequences that library at high depth, and maps the resulting reads back to a reference genome or transcriptome. The output is a quantitative, nucleotide-resolution view of every RNA molecule present at the time of sampling, which genes are expressed, at what level, and in which structural form¹¹. The transcriptome is dynamic and context-dependent, making RNA-seq uniquely suited to capturing a cell’s functional state in response to disease, treatment, or environmental change. Unlike genomics, which reads a largely static DNA blueprint, transcriptomics reveals the cell’s activity at a given moment. ¹¹

3. RNA-Seq Techniques

mRNA SEQUENCING

The technique focuses on messenger RNA, which is the central molecule for protein synthesis. Thus, it allows one to investigate active gene expression and gene translation under various conditions. The technique is used in illness models for alterations in gene expression, identifying therapeutic targets for drug development, and testing drugs to evaluate the treatment effects on the activity of genes. For example, mRNA-Seq is essential to understand how cancerous cells respond to drugs and provides the vital information during the drug development process.

Total RNA sequencing

Total RNA-Seq collects all types of RNA, i.e., mRNA, rRNA, and tRNA. This technique provides a complete view of the transcriptome, which is very important in the study of non-coding RNAs and changes in RNA. This method makes use of Total RNA-Seq in the study of regulatory RNAs that regulate gene expression and cellular processes without encoding proteins. The choice between mRNA-Seq and total RNA sequencing depends on the study’s objectives. mRNA-Seq is used for genes in treatments or diseases, while total RNA sequencing is suitable for studying RNA species’ complexity in multicellular systems. Both require advanced bioinformatic analysis for interpretation⁴.

Recent developments allow working with challenging samples, like degraded RNA, FFPE or single cells. With these advancements, low-quality RNA can be useful by stepwise mapping, and in single-cell cancer studies “RAG-seq” and “snHH-seq” enhance the sensitivity. Automated platforms provide large-scale analyses to make it easier for researchers to determine the complexity of the transcriptome and therapeutic targets ^4,5,6.

Poly(A) RNA Seq

Poly(A) RNA sequencing targets RNAs with poly(A) tails. These include mRNAs and lncRNAs. This technology relies on the stability and relevance of poly(A) tails for the 3′ ends of eukaryotic mRNAs. It strengthens RNA stability, protects from degradation, helps in exporting mRNA and facilitates its translation. Poly(A) RNA-seq will uncover the complexity of a transcriptome, levels of gene expression, and alternative splicing events. It is useful for the discovery of splice variants and for post-transcriptional regulation and characterization of the isoforms. Since it does not recover degraded mRNAs or non-poly(A) transcripts, it may introduce noise and selection bias in gene expression analysis. The preparation of libraries for poly(A) RNA-seq involves enrichment of mRNA from total RNA, which focuses on poly(A)+ transcripts. This makes poly(A) RNA-seq a reliable technique to study protein-coding genes and their regulatory mechanisms though excluding non-poly(A) RNAs ⁷.

3’ UTR mRNA sequencing

3′ UTR mRNA sequencing, commonly known as 3′ mRNA-seq, is a specialized approach that focuses on the 3′ untranslated region of polyadenylated RNA molecules. It enables efficient and reliable assessment of gene expression with fewer reads per sample, decreasing the costs of sequencing but keeping substantial transcriptome-wide insights equivalent to full-length RNA-seq approaches. It is notably valuable for examining processes of regulation involving the 3′ UTR, including alternative polyadenylation, a fundamental method of gene expression regulation. Researchers have mapped 3′ UTR diversity over a wide range of different samples ranging from C. elegans to human tumor types. Despite the high-quality curated 3′ UTR resources supplied by databases such as UTRdb and Ensembl, comparative genome-wide research outside model organisms remains challenging due to the poor annotations ⁸.

Bulk RNA Barcoding and Sequencing

Bulk RNA barcoding and sequencing (BRB-seq) is cost-effective for the high throughput of transcriptomics. It provides affordability comparable to single-cell RNA sequencing plus the traditional efficiency in bulk RNA sequencing. BRB-seq consequently provides extremely high-quality libraries that are just as good as those generated by technologies like TruSeq; a reproducible and inexpensive option for RNA-seq. This method is important for discovering molecular pathways of health and illness. These enable versatility for researching homogeneous cell groupings and can easily face tough sample preparations or budget restrictions. Additionally, computational approaches can improve bulk RNA-seq data by assessing cell-type diversity. These applications make BRB-seq a cheap, scalable, and reliable approach for transcriptome studies and give complementary utility to newer technologies ⁹.

Small RNA Sequencing

Small RNA sequencing (Seq) is a technique that has high sensitivity and dynamic range to isolate and sequence small RNA molecules, including microRNAs (miRNAs). It allows for the genome-wide profiling of known and novel miRNA variants, which gives deep insights into the small RNA transcriptome. However, it is also subject to technical bias and incomplete miRNA representation, limiting its full potential. These biases have been reduced and the technique is proving to be effective and comprehensive technology. It first appeared in 2005 and later on it was mostly used with more than 700 human miRNA profiles deposited in GEO ¹⁰.

4. RNA-seq vs qPCR: when does RNA-seq win?

qPCR has long been considered the golden standard to measuring gene expression. qPCR is cheap, relatively fast, scalable and highly sensitive. However, qPCR only makes sense when 1) you already know your exact target and 2) you are only interested in a limited number of targets. As for each target, a primer pair should be available or developed, qPCR is often limited to more common targets. Conversely, RNA-seq is a broad approach which measures the whole, without the need for specific primers or targets. And while the cost of RNA-seq will always be more expensive that running a single qPCR experiment, the balance can quickly shift towards RNA-seq when multiple targets are of interest. RNA-seq is typically the right choice when:

You need discovery, not just confirmation. RNA-seq identifies differentially expressed genes you did not know to look for
You want isoform and splice variant resolution, which standard qPCR cannot provide
You need to detect non-coding RNAs, fusion transcripts, or novel transcripts
Your sample is limited. RNA-seq generates data on thousands of genes from a single experiment

The most robust workflow is to use RNA-seq for discovery and qPCR for targeted validation of selected hits. The two methods are complementary, not competing ¹².

5. Choosing the Right RNA-Seq Technique

The choice of the appropriate approach to RNA-Seq is a prerequisite for achieving success and depends on depth of analysis, RNA type, and complexity in the transcriptome. Total RNA-Seq is the most comprehensive technique as it covers both coding and non-coding RNAs. This is highly ideal for studying complex transcriptomes, regulatory processes, and alternative splicing patterns. In contrast, mRNA-Seq targets polyadenylated mRNA and is, therefore, the optimal choice for gene expression study, especially in cases of constrained resources such as sample size or budget. Specialized studies might make use of small RNA-Seq on short regulatory RNAs, whereas technologies such as ribosomal RNA depletion enhance data quality. Researchers must consider the nature of their samples, sequencing depth and cost to match their choice with experimental goals. Balancing these factors will ensure accurate, efficient and useful insights for a range of RNA-based studies ^4,10.

6. How to choose an RNA-seq service provider: 5 questions to ask

Do they offer end-to-end service from sample QC and library preparation through to bioinformatics reporting?
Which sequencing platforms do they run, and can they advise on the most appropriate one for your sample type and research question?
Do they have validated protocols for challenging sample types such as FFPE, liquid biopsy, or low-input material?
Is the work performed under ISO certification and, where required, GCLP-compliant conditions?
What bioinformatics deliverables will you receive, and are custom pipelines available?

OHMX.bio answers yes to all five. As an ISO-certified, GCLP compliant CRO with in-house wet lab and bioinformatics, we run Illumina, Element Biosciences AVITI, and Oxford Nanopore platforms across the full range of RNA-seq applications.

7. Conclusion

RNA sequencing has revolutionized the understanding of the transcriptome, providing insights into gene expression, regulatory mechanisms, and RNA diversity. Tailoring RNA-Seq techniques to specific research goals in coding RNAs, non-coding RNAs, or small RNA populations may unlock meaningful discoveries across a wide range of applications, from disease diagnostics to therapeutic development. Advancements in technology allow for further insights through innovative long-read sequencing processes. Aligning research goals with the right sequencing strategies can provide cost-effective studies and impact scientific discovery. Researchers, biotech professionals, and labs continue advancing basic biology and precision medicine by recognizing and harnessing strengths within RNA-Seq. As RNA-seq methods are maturing, their introduction into clinical studies has expanded drastically over the last years.

Frequently asked questions about RNA sequencing?

What is the difference between RNA-seq and microarray?

Microarrays measure hybridisation to predefined probes, they cannot detect novel transcripts and have limited dynamic range. RNA-seq sequences every RNA molecule present, offering superior dynamic range, isoform resolution, and the ability to detect novel transcripts.

How much input RNA do I need for RNA-seq?

Standard bulk RNA-seq requires 100 ng–1 µg of total RNA. OHMX.bio offers low-input and single-cell protocols working from as little as 1 ng or from individual cells.

What is the difference between mRNA-seq and total RNA-seq?

mRNA-Seq focuses on polyadenylated transcripts and is optimal for protein-coding gene expression. Total RNA-Seq covers all RNA species including non-coding RNAs, making it more comprehensive but requiring deeper sequencing depth.

Can RNA-seq be used for FFPE samples?

Yes. FFPE RNA is typically degraded, but OHMX.bio has validated protocols for FFPE RNA-seq that deliver reliable results even from archival tissue.

What is Ribo-seq and how does it differ from RNA-seq?

RNA-seq measures transcript abundance. Ribo-seq (ribosome profiling) sequences only the mRNA fragments actively occupied by ribosomes, revealing which transcripts are being translated and at what rate. Combining RNA-seq with Ribo-seq enables translation efficiency analysis — a metric neither provides alone.

Is single-cell RNA sequencing the same as RNA sequencing?

Yes, the main difference between single-cell and so-called bulk RNA sequencing is that in single cell, an extra barcode is added to each RNA-strand which is unique to the cell it was extracted from. This allows each sequenced strand to be traced back to its specific cell of origin. In bulk RNA sequencing, all RNA is sequenced together without this barcode.

Do you have questions about RNA sequencing?

Fill out the form below and our experts will get back to you as soon as possible!

References

[1] Deshpande, D., Chhugani, K., Chang, Y., Karlsberg, A., Loeffler, C., Zhang, J., … & Mangul, S. (2023). RNA-seq data science: From raw data to effective interpretation. Frontiers in Genetics, 14, 997383.

[2] Louis, I. V. S., Gorzalski, A., & Pandori, M. (2021). Diagnostic applications for RNA-Seq technology and transcriptome analyses in human diseases caused by RNA viruses. Applications of RNA-Seq in Biology and Medicine, 122-138.

[3] Hong, M., Tao, S., Zhang, L., Diao, L. T., Huang, X., Huang, S., … & Zhang, H. (2020). RNA sequencing: new technologies and applications in cancer research. Journal of hematology & oncology, 13, 1-16.

[4] Choosing between total RNA-Seq and mRNA-Seq. TECHNICAL NOTE. Assessed From https://assets.thermofisher.com/TFS-Assets/BID/Technical-Notes/collibri-stranded-rna-library-prep-kit-total-rna-seq-mrna-seq-technical-note.pdf.

[5] Xu, P., Yuan, Z., Lu, X., Zhou, P., Qiu, D., Qiao, Z., … & Yu, Y. (2024). RAG-seq: A NSR Primed and Transposase Tagmentation Mediated Strand-specific Total RNA Sequencing in Single Cell. Genomics, Proteomics & Bioinformatics, qzae072.

[6] Chen, H., Fang, X., Shao, J., Zhang, Q., Xu, L., Chen, J., … & Guo, G. (2024). Pan‐Cancer Single‐Nucleus Total RNA Sequencing Using snHH‐Advanced Science, 11(5), 2304755.

[7] Yu, F., Zhang, Y., Cheng, C., Wang, W., Zhou, Z., Rang, W., … & Zhang, Y. (2020). Poly (A)-seq: A method for direct sequencing and analysis of the transcriptomic poly (A)-tails. PloS one, 15(6), e0234696.

[8] Huang, Z., & Teeling, E. C. (2017). ExUTR: a novel pipeline for large-scale prediction of 3′-UTR sequences from NGS data.BMC genomics, 18, 1-11.

[9] Janjic, A., Wange, L. E., Bagnoli, J. W., Geuder, J., Nguyen, P., Richter, D., … & Enard, W. (2022). Prime-seq, efficient and powerful bulk RNA sequencing. Genome biology, 23(1), 88.

[10] Benesova, S., Kubista, M., & Valihrach, L. (2021). Small RNA-sequencing: approaches and considerations for miRNA analysis. Diagnostics, 11(6), 964.

[11] Stark, R., Grzelak, M., & Hadfield, J. (2019). RNA sequencing: the teenage years. Nature Reviews Genetics, 20(11), 631–656. https://doi.org/10.1038/s41576-019-0150-2

[12] Zhao, S., Fung-Leung, W. P., Bittner, A., Ngo, K., & Liu, X. (2014). Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells. PLoS ONE, 9(1), e78644. https://doi.org/10.1371/journal.pone.0078644