Basics and Applications of RNA Sequencing Methods

May 9, 2025

The global next-generation sequencing (NGS) informatics and clinical genomics market is forecasted to grow by nearly 5.9 billion U.S. dollars by 2028 from 836 million U.S. dollars in 2017, according to Statista. The increasing demand for precision medicine and advancements in genomic research fuel this rapid adoption and surge.

RNA sequencing (RNA-seq) is a powerful next-generation sequencing (NGS) technique for analyzing the presence, activity, and quantity of RNA in a biological sample. Compared to traditional methods like microarrays, RNA-seq is more detailed and accurate. 

This technique allows the study of the transcriptome, the complete set of RNA molecules, including messenger RNA (mRNA), non-coding RNA, and small RNA. By converting RNA into complementary DNA (cDNA) and sequencing it, researchers can obtain a snapshot of the gene.

Below, you’ll explore RNA sequencing comprehensively, its types, advancements, applications, and more. 

Types of RNA Sequencing Techniques

Types of RNA Sequencing Techniques

                                                                  Sources: Pixabay

RNA sequencing (RNA-seq) has several different types of techniques that are designed to achieve other aims and offer unique perks that you will explore. Here are some of the main types of RNA sequencing that you should know:

  1. Whole Transcriptome Sequencing (WTS)

Whole-transcriptome RNA sequencing (WTS) provides several key advantages for researchers studying gene expression. By sequencing the entire RNA content of a sample, WTS offers a comprehensive view of the transcriptome. This method enables the identification of novel RNA molecules, including mRNA, non-coding RNA, and small RNA, which might otherwise go undetected. When paired with advanced bioinformatics tools, WTS is especially valuable for uncovering additional mutations in genodermatoses.  

  1. mRNA-Seq (Messenger RNA Sequencing)

mRNA sequencing (mRNA-Seq) has quickly become a widely preferred method for examining transcriptomes in various conditions.  The technique is a highly sensitive and precise technique for measuring gene expression. It can also identify known and novel transcript isoforms, gene fusions, and other genomic features, including allele-specific expression. 

  1. Single-cell RNA-Seq (scRNA-Seq)

Single-cell RNA sequencing (scRNA-seq) of cell populations provides expression profiles averaged across cells. Still, this approach fails to capture the diversity of cell types or subtypes within a population. It also loses co-expression patterns between genes when cells are aggregated. 

  1. Exome RNA-Seq

Exome RNA-seq focuses on sequencing the RNA from exonic regions of the genome, i.e., the parts of the genome that are translated into proteins. Exome sequencing has recently garnered attention in the study of various diseases, particularly in identifying genetic variations linked to monogenic diseases with Mendelian inheritance. It has also been applied to uncover genetic variations that serve as risk factors for complex diseases. 

  1. Targeted RNA-Seq

Targeted RNA sequencing (RNA-Seq) is often used when focusing on a specific set of genes or lowly expressed genes that may be missed in whole transcriptome sequencing. Two main approaches are used for targeted RNA-Seq: target capture and amplicon sequencing.

  1. Digital gene expression (DGE)-Seq

Digital gene expression sequencing (DGE-Seq), also known as Tag-Seq, is a deep sequencing method that is derived from Serial Analysis of Gene Expression (SAGE). This method preserves RNA strandedness by design, making it particularly useful for simple gene expression profiling and cost-effective applications. DGE-Seq has been commercialized and is preferred when the goal is basic gene expression analysis.

Each of these RNA-seq techniques provides unique advantages depending on the research question and the level of detail required in the analysis. Now below you will find some of the applications of this technology. 

Applications of RNA Sequencing

Applications of RNA Sequencing

Sources: NIH

There are multiple applications of RNA sequencing in various fields of biological research, providing in-depth insights into gene expression, splicing, mutations, and regulatory mechanisms. Some of the primary applications include:

1. Differential Gene Expression (DGE) Analysis: RNA-seq allows the comparison of gene expression profiles in different conditions, such as disease versus normal states, developmental stages, or response to experimental stimuli. It enables the identification of genes, isoforms, and their precise abundance levels across samples, contributing to the understanding of complex biological phenomena.

For example, RNA-seq analysis allowed the researchers to compare the gene expression profiles of Caragana korshinskii, a plant species known for its tolerance to drought stress, under drought-treated and control conditions. By analyzing the differentially expressed genes (DEGs), they were able to identify specific genes that are upregulated or downregulated in response to drought.   

2. Alternative Splicing: RNA-seq is crucial for studying alternative splicing; this process contributes to revealing cellular diversity. This method helps identify different splicing events, uncover tissue-specific splicing patterns, and detect abnormal splicing events associated with diseases. Alternative splicing analysis is particularly important in understanding diseases like cancer, neurodegenerative diseases, and genetic disorders.

For example, the researchers applied RNA-seq to uncover novel and alternative splicing events in an organism, especially in cases where existing annotations are incomplete. The findings contribute valuable insights into the dynamic and complex transcriptome of Plasmodium falciparum, which could potentially lead to an improved understanding of its biology and the development of better therapeutic strategies against malaria.

3. Variant Detection and Allele-Specific Expression (ASE): RNA-seq can also detect mutations and variants at the transcript level, including single nucleotide polymorphisms (SNPs). This technique can also identify allele-specific expression (ASE). ASE plays a critical role in understanding disease mechanisms and how genetic variations impact gene expression.

For example, Sheng et al. used a bias-reduced generalized linear model to identify somatic mutations, while Yizhak et al. designed an alignment strategy to reduce read errors in RNA sequencing data. Additionally, Muyas et al. developed a machine-learning model to differentiate somatic variants from germline variants in RNA-seq data. These approaches enhance the ability to detect mutations and improve the accuracy of mutation identification in cancer research.

4. Pathway and Gene Set Analysis: RNA-seq data can be integrated into pathway analysis to understand how genes work together in biological pathways. By analyzing gene sets and pathways, researchers can identify key regulatory networks that control cellular functions and discover how subtle changes in gene expression can affect cellular processes.  

For example, iDEP (integrated Differential Expression and Pathway analysis) is a tool that uses RNA sequencing (RNA-seq) data for pathway analysis to connect 63 R/Bioconductor packages, two web services, and comprehensive annotation and pathway databases for over 220 plant and animal species. RNA-seq datasets were analyzed to explore the molecular pathways involved in various biological processes. 

5. Co-expression Network Analysis: RNA-seq allows the construction of gene co-expression networks, where genes that show similar expression patterns across samples are grouped together. This method is useful for identifying genes involved in similar biological processes, discovering gene regulators, and understanding the relationships between different genes.  

For example, researchers in one experiment collected great saphenous veins (GSV) from coronary artery bypass grafting (CABG) patients and varicose veins from patients undergoing conventional stripping surgery. RNA sequencing (RNA-Seq) was used to generate comprehensive transcriptomic data for both types of veins. Subsequently, Weighted Gene Co-expression Network Analysis (WGCNA) and additional analyses were performed to identify key regulatory genes and pathways associated with varicose veins.

Above, you explore all the real applications of this advanced RNA sequencing technology; now, below, you explore the procedure or workflow for RNA sequencing technology. 

Implementation Workflow for RNA Sequencing

Implementation Workflow for RNA Sequencing

Source: Wikipedia Commons

Below, you get access to a comprehensive explanation of the full workflow of RNA sequencing (RNA-seq), detailing the three main sections of the process: Experimental Biology, Computational Biology, and Systems Biology. These three main sections cover specific data preprocessing steps, such as quality assessment, reads mapping, normalization, and read counting, all of which are critical to ensure the accuracy of RNA-seq results.  

Let’s explore the workflow below:

1. Experimental Biology: The workflow starts with several mini procedures, including RNA collection, first strand synthesis, and library construction. It is then followed by sequencing using various platforms, such as Illumina, Ion Torrent, PacBio, and MinION. 

These platforms generate millions of short reads. RNA preparation methods (such as poly-A selection and ribosomal RNA depletion) depend on the type of sequencing, RNA subtype, and sequencing purpose. The quality of the RNA sample plays a critical role in obtaining accurate data.

2. Computational Biology: Once raw reads are generated, quality control measures are applied to remove low-quality sequences, contaminants, and adaptors. Then, the sequencing data is mapped to a reference genome or transcriptome. The reads are indexed at the gene, exon, or transcript level, depending on the study's goal. 

Differential gene expression analysis, alternative splicing detection, and integration with other omics data (e.g., epigenomic or proteomic) are carried out. These steps are followed by pathway or network analysis to provide biological insights.

3. Systems Biology: This section involves the use of bioinformatics tools and statistical models to analyze the data and generate biological interpretations, such as identifying key regulatory genes and pathways involved in specific biological processes or diseases.

Overall, these are the steps that you need to cover to understand the genomics activities of the sample. Now, below, you’ll explore some challenges and considerations that you can take while doing RNA-sequencing analysis. 

Challenges and Considerations in RNA-Seq

Challenges and Considerations in RNA-Seq

RNA sequencing (RNA-Seq) is a powerful tool for analyzing transcriptomes, but several challenges must be addressed to ensure accurate data processing and meaningful results. Below, you’ll explore some of the challenges of this technology. 

1. Data Storage and Computational Requirements: One of the significant challenges in RNA-Seq is the large volume of raw image files generated by next-generation sequencing (NGS) platforms, which can require terabytes of storage. To handle the computational demands of large-scale RNA-Seq data analysis efficiently, high-performance computing (HPC) clusters or cloud computing platforms are typically used rather than just a small cluster of computers. 

2. Sequencing Platform Differences: Another issue lies in the diversity of sequencing platforms and the variations in data quality and format. Each platform, whether it’s PacBio, Illumina, or Oxford Nanopore, produces data with specific error profiles. These platform differences lead to challenges in mapping NGS reads accurately and reliably, often requiring specialized algorithms designed for each platform. As a result, there is no universal "one-size-fits-all" software, making individualized data processing pipelines necessary.

3. Software and Pipeline Issues: While several RNA-Seq software packages exist, most require significant time and effort to set up, as the documentation is often unclear or sparse. Additionally, the variety of data formats across platforms complicates data management and analysis. Clearer documentation and community adoption of standard input/output formats would significantly alleviate these challenges, making it easier for researchers to manage and analyze data.  

Overall, while RNA-Seq is a powerful tool for transcriptomic analysis, challenges in data storage, computational capacity, and software limitations must be addressed to improve the accuracy and efficiency of RNA-Seq experiments. Now you have landed on the continuing section that you will explore; it will offer you a sum-up of the knowledge you gained in this article. 

Conclusion

RNA sequencing (RNA-seq) is a powerful technique for analyzing gene expression and the transcriptome. It includes various methods, such as whole transcriptome sequencing, mRNA-Seq, and single-cell RNA-seq, each suited for specific applications. RNA-seq aids in differential gene expression analysis, alternative splicing, variant detection, and pathway analysis. 

While offering valuable insights, challenges like data storage, computational power, and platform differences remain. These can be overcome as the technology evolves and considerations are made for accurate data processing and meaningful results. 

Understanding RNA, genomes, and genomic expression is important research. If you are looking for a comprehensive RNA sequencing solution, Biostate.ai is a great space for you. The platform offers high-quality insights and reliable, actionable data without breaking the budget. Get Your Today! 

Disclaimer: This article provides general information about RNA sequencing and its applications. It is not intended as medical advice. For any medical concerns, always consult with a licensed healthcare professional.

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