RNA sequencing: better choice than qPCR?

1. Introduction to RNA sequencing

RNA sequencing (RNAseq) is a revolutionary technique that provides deep insight into gene expression and regulation. Once considered to be the messenger between DNA and proteins, RNA now came to be recognized for its crucial regulatory roles in numerous cellular processes, both in normal and disease states. This technique has now become an essential tool in the contemporary field of biology, mainly for understanding gene regulation. Because RNAseq measures the types and numbers of RNA molecules, it is now possible to consider intricate biological entities at unprecedented depths. It enables research and development in nearly all industries, from healthcare and pharmaceuticals to agriculture. The increasing demand for RNAseq reflects its importance in developing scientific knowledge and uncovering the molecular mechanisms behind various traits or diseases^1-3. We will explain what RNA-seq is and its applications in different fields to advance the understanding of the readers in this area.

2. What is RNA-seq?

The length and quantity of RNA in a cell or tissue are sequenced in respect to its genomic context using the transcriptome sequencing method known as RNAseq. The process of RNA expression profiling or gene expression analysis starts with the extraction of RNA from the biological sample. The extracted RNA is then typically converted to complementary DNA (cDNA) which in turn is sequenced using NGS methods. Alternatively, some sequencing techniques allow for the direct sequencing of RNA (DirectRNA) without the cDNA conversion, thus also providing information on the modifications of the RNA.

In the area of transcriptomics, RNAseq has great potential to determine the extent of gene expression in alternative splicing of mRNAs and non-coding RNAs and untranslated regions of new gene transcripts. In recent years many technological advances have been made providing an improved and sensitive resolution. There are several commonly used sequencing platforms for RNA-seq such as Illumina, Element Biosciences, Nanopore, and PacBio. Illumina’s short-read technology has a high level of accuracy (though surpassed by Element Biosciences) and has a high throughput. However, it is not ideal for quantitative gene expression analysis. Long-read sequencing, in contrast to Illumina’s short-read sequencing, improves on the coverage of complex genomic regions as well as full-length cDNA or RNA sequences. These technologies work in concert and include combinations of coding and non-coding transcripts along with strategies using short and long reads thereby enhancing overall accuracy ^4,6.

3. Applications of RNA-seq

Gene Expression Profiling

The profiling of gene expression plays a significant role in understanding the physiological as well as pathological processes of the body. RNAseq has become a compelling replacement for conventional microarray-based analyses of transcriptional activities. The technique has revolutionized cancer research by featuring differential gene expression, tumor heterogeneity, drug resistance, and tumor microenvironment studies7. Furthermore, it is effective in identifying early and high-risk mutations, discovering new cancer biomarkers as well as novel therapeutic targets, monitoring the disease course, and making meaningful decisions regarding targeted therapy. In the toxicogenomic context, RNAseq has been applied to find differentially expressed genes in tissues after treatment with various compounds for estimating possible toxic impacts and the underlying mechanisms^8,9.

Transcriptome Analysis

The transcriptome is described as the collection of RNA present in an organism, a tissue, or even a cell. The introduction of NGS technology has extended the spectrum of transcriptomics, where RNAseq became the core method for transcriptome analysis. This is important in the understanding of the transcript, isoforms, and non-coding RNAs, hence altering the paradigm of gene expression studies. RNAseq allows the detection of different transcript variants originating from the same gene ¹⁰. Apart from these, RNAseq has been successfully used for the discovery of long non-coding RNAs (lncRNAs) which are key regulatory molecules¹¹. Transcriptomic profiling also helps in the identification of stem cells and other complex diseases-associated biomarkers which help in the understanding of targets for the therapies ¹².

Comparative Transcriptomics

RNAseq is particularly useful in comparative transcriptomics since gene expression can be readily compared among species, different time points, or different conditions. As such, it enables transcriptomics investigations even in species that are not classically studied or lack genomic information. RNAseq allows for such comparisons across species, and it is observed that there are both similarities and differences in some gene expressions. Comparing multiple organs within a species, it has been shown that core organs share the same functional traits across a variety of species ^13,16.

Single-Cell RNA-Seq

Single-cell RNA sequencing (scRNAseq) has become a prominent tool for studying the complexity and heterogeneity of RNA transcripts in tissues and especially in individual cells. It looks at the composition and activity of various types of cells in different tissues and organs. This approach is highly valuable in the study of cancer by allowing the discovery of novel cell subset populations, stratification of cancer microenvironments, and the identification of mutations at the single-cell level. It has expanded knowledge concerning tumors and processes leading to their forming¹⁷. In immunology, transcripts of immune cells have been thoroughly investigated, taking the focus away from markers only on the surface of the cell discovering new subtypes, and enhancing understanding of immune and inflammatory responses ¹⁸.

Other Notable Applications

Beyond the mentioned categories, RNAseq also assists in studying the mechanisms of gene regulation in the epigenomic context¹⁹. It is helpful in pharmacogenomics as RNAseq identifies genetic polymorphisms that affect sensitivity to therapy, thus signaling the future of targeted therapies. By customizing medicines according to gene expression profiles of the patient, RNA-seq further advances precision medicine ²⁰.

4. Costs and pricing of RNA-seq

Costs associated with RNAseq projects are very variable, particularly in the organization of tasks such as sample preparation, sequence depth, and data analysis. General RNAseq is comparatively cheaper compared to scRNA-seq (~$5,000) and a relatively larger transcriptomic project as a number of standards and advanced techniques are needed to attain such a throughput. Costs for sample preparation can vary depending on the degree of complexity of the library preparation protocols. Even though the costs of RNAseq are declining with the advancements in the area, the increase in techniques and focal points, such as scRNAseq and targeted isoform detection mean that RNAseq can still present a significant cost. It is important to discuss your project in detail and see what is actually needed to get an insight into the desired biological insights ^21,27.

5. Conclusion

The versatility and significance of RNAseq in modern science are obvious. This advanced technology has become an important tool for discovering new biomarkers, examining the disease causes, and revealing the evolutionary links. Its application spans various domains, giving researchers the capacity to delve deeper into the complexity of gene expression and regulation. Breakthroughs into precision medicine, improved agricultural practices, and a better understanding of fundamental biological processes are highly anticipated through these insights originating from RNAseq. If you haven’t thought about using RNA-seq in your own study yet, this could be the ideal opportunity to do so!

References

[1] Spitale, R. C., & Incarnato, D. (2023). Probing the dynamic RNA structurome and its functions. Nature Reviews Genetics, 24(3), 178-196.

[2] Kukurba, K. R., & Montgomery, S. B. (2015). RNA sequencing and analysis. Cold Spring Harbor Protocols, 2015(11), pdb-top084970.

[3] Sripathi, V. R., Anche, V. C., Gossett, Z. B., & Walker, L. T. (2021). Recent applications of RNA sequencing in food and agriculture. Applications of RNA-Seq in Biology and Medicine, 97.

[4] 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.

[5] Amarasinghe et al. (2020). Opportunities and challenges in long-read sequencing data analysis. Genome biology, 21(1), 30.

[6] De Maio, N., Shaw, L. P., Hubbard, A., George, S., Sanderson, N. D., Swann, J., … & Stoesser, N. (2019). Comparison of long-read sequencing technologies in the hybrid assembly of complex bacterial genomes. Microbial genomics, 5(9), e000294.

[7] 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.

[8] Rukhsar, L., Bangyal, W. H., Ali Khan, M. S., Ag Ibrahim, A. A., Nisar, K., & Rawat, D. B. (2022). Analyzing RNA-seq gene expression data using deep learning approaches for cancer classification. Applied Sciences, 12(4), 1850.

[9] Rao, M. S., Van Vleet, T. R., Ciurlionis, R., Buck, W. R., Mittelstadt, S. W., Blomme, E. A., & Liguori, M. J. (2019). Comparison of RNA-Seq and microarray gene expression platforms for the toxicogenomic evaluation of liver from short-term rat toxicity studies. Frontiers in genetics, 9, 636.

[10] Dehghanzad, R., Khalafiyan, A., & Khanahmad, H. (2023). The Necessity of Using Strand-Specific cDNA for Achieving Accurate Transcriptome Analysis Result. Advanced Biomedical Research, 12(1), 108.

[11] Mostafa, S. M., Wang, L., Tian, B., Graber, J., & Moore, C. (2024). Transcriptomic analysis reveals regulation of adipogenesis via long non-coding RNA, alternative splicing, and alternative polyadenylation. Scientific Reports, 14(1), 16964.

[12] Sharma, S., Kalpdev, D., & Choudhary, A. (2024). Transcriptomic profiling–based identification of biomarkers of stem cells. In Computational Biology for Stem Cell Research(pp. 203-214). Academic Press.

[13] Todd, E. V., Black, M. A., & Gemmell, N. J. (2016). The power and promise of RNA‐seq in ecology and evolution.Molecular ecology, 25(6), 1224-1241.

[14] Sudmant, P. H., Alexis, M. S., & Burge, C. B. (2015). Meta-analysis of RNA-seq expression data across species, tissues and studies. Genome biology, 16, 1-11.

[15] Chung, M., Bruno, V. M., Rasko, D. A., Cuomo, C. A., Muñoz, J. F., Livny, J., … & Dunning Hotopp, J. C. (2021). Best practices on the differential expression analysis of multi-species RNA-seq. Genome biology, 22(1), 121.

[16] Darbellay, F., & Necsulea, A. (2020). Comparative transcriptomics analyses across species, organs, and developmental stages reveal functionally constrained lncRNAs. Molecular biology and evolution, 37(1), 240-259.

[17] Huang, D., Ma, N., Li, X., Gou, Y., Duan, Y., Liu, B., … & Zhang, X. (2023). Advances in single-cell RNA sequencing and its applications in cancer research. Journal of hematology & oncology, 16(1), 98.

[18] Yamawaki, T. M., Lu, D. R., Ellwanger, D. C., Bhatt, D., Manzanillo, P., Arias, V., … & Li, C. M. (2021). Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling. BMC genomics, 22, 1-18.

[19] Zhao, L. Y., Song, J., Liu, Y., Song, C. X., & Yi, C. (2020). Mapping the epigenetic modifications of DNA and RNA. Protein & cell, 11(11), 792-808.

[20] Kazim, I., Gande, T., Reyher, E., Bhutia, K. G., Dhingra, K., & Verma, S. (2024). Advancements in sequencing technologies:: from genomic revolution to single-cell insights in precision medicine. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(4), 108-124.

[21] Takele Assefa, A., Vandesompele, J., & Thas, O. (2020). On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments. BMC genomics, 21, 1-14.

[22] Gierahn, T. M., Wadsworth II, M. H., Hughes, T. K., Bryson, B. D., Butler, A., Satija, R., … & Shalek, A. K. (2020). Seq-Well: A Portable, Low-cost Platform for Single-Cell RNA-Seq of Low-Input Samples. Understanding the Cellular Ecology of Mtb Granulomas Using Single-Cell Sequencing, 28.

[23] Li, X., & Wang, C. Y. (2021). From bulk, single-cell to spatial RNA sequencing. International journal of oral science, 13(1), 36.

[24] Genomic Technologies | UCSF Functional Genomics Core

[25] Pricing | Genomics Analysis Core (GAC) (pitt.edu)

[26] Budgeting for an mRNA-seq project? How much does RNA Seq cost? (alitheagenomics.com)

[27] Microsoft Word – Summary sheet RNA seq costs.docx (bu.edu)

Search all posts

Dark antigens: Expanding the horizons of immunopeptidomics and multi-omics discovery

Post on February 25, 2026

Best practices for cell line quality control in 2026

Post on January 16, 2026

Immunopeptidomics: Unlocking the antigen landscape for precision immunotherapy

Post on October 21, 2025

Do you have questions about RNA sequencing?

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

Search all posts

Dark antigens: Expanding the horizons of immunopeptidomics and multi-omics discovery

Post on February 25, 2026

Best practices for cell line quality control in 2026

Post on January 16, 2026

Immunopeptidomics: Unlocking the antigen landscape for precision immunotherapy

Post on October 21, 2025

Cookie	Description	Duration	Type
cookielawinfo-checkbox-necessary	This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".	1 hour	Necessary
__utma	This cookie is set by Google Analytics and is used to distinguish users and sessions. The cookie is created when the JavaScript library executes and there are no existing __utma cookies. The cookie is updated every time data is sent to Google Analytics.	2 years	Performance
__utmc	The cookie is set by Google Analytics and is deleted when the user closes the browser. The cookie is not used by ga.js. The cookie is used to enable interoperability with urchin.js which is an older version of Google analytics and used in conjunction with the __utmb cookie to determine new sessions/visits.		Performance
__utmz	This cookie is set by Google analytics and is used to store the traffic source or campaign through which the visitor reached your site.	5 months	Performance
__utmt	The cookie is set by Google Analytics and is used to throttle request rate.	10 minutes	Performance
__utmb	The cookie is set by Google Analytics. The cookie is used to determine new sessions/visits. The cookie is created when the JavaScript library executes and there are no existing __utma cookies. The cookie is updated every time data is sent to Google Analytics.	30 minutes	Performance

TESTING SERVICES

TECHNOLOGIES

EXPERTISE

QUALITY

TESTING SERVICES

TECHNOLOGIES

EXPERTISE

QUALITY

RNA sequencing: better choice than qPCR?

1. Introduction to RNA sequencing

2. What is RNA-seq?

3. Applications of RNA-seq

Gene Expression Profiling

Transcriptome Analysis

Comparative Transcriptomics

Single-Cell RNA-Seq

Other Notable Applications

4. Costs and pricing of RNA-seq

5. Conclusion

References

Search all posts

Popular news & events

Tags

Do you have questions about RNA sequencing?

Search all posts

Popular news & events

Tags

TESTING SERVICES

EXPERTISE

OHMX.bio

CONTACT