DNA and RNA sequencing are fundamental methods in genetic research. They enable scientists to explore the structure, function, and diversity of genetic material. DNA sequencing reveals organisms’ static blueprint, while RNA sequencing provides a dynamic snapshot of gene expression and microbial activity.
A recent comparison of human cervical samples demonstrated key differences between these methods: DNA sequencing detected 93% of human sequences, while RNA sequencing detected 76% without DNase treatment and only 11% with DNase treatment.
Notably, bacterial sequence detection increased 17-fold and 91-fold with RNA sequencing without and with DNase-treatment, respectively, compared to DNA sequencing. These findings highlight the critical role of sequencing choice in research outcomes.
By selecting the appropriate method, researchers can more accurately profile microbial communities, understand disease mechanisms, and drive innovations in diagnosis and personalized medicine.
In this article, you will learn more in-depth about DNA and RNA sequencing techniques, their types, applications in clinical research, and more.
Traditional DNA and RNA Sequencing Techniques
Source: NIH – Overview of RNA Sequencing
Traditional DNA and RNA sequencing techniques, like Sanger and Maxam-Gilbert methods, were used early to determine nucleotide sequences. Sanger sequencing, the most widely used, offered high accuracy by incorporating chain-terminating nucleotides. RNA was first converted to cDNA before sequencing. Though slower and lower in throughput, these techniques laid the foundation for modern genomics.
In this section, you will cover both of these techniques, starting with DNA sequencing.
Traditional DNA Sequencing Techniques
DNA sequencing has transformed the fields of genetics, biotechnology, forensics, and medicine. Before the rise of next-generation sequencing (NGS), several traditional methods laid the groundwork for modern genomics. Each technique offered unique strengths in terms of accuracy, speed, read length, and cost.
Below is an overview of the four foundational DNA sequencing techniques.
- Sanger Sequencing (Chain Termination Method)
The Sanger method, developed in the 1970s, is one of the most influential DNA sequencing techniques. It relies on enzymatic polymerization where DNA polymerase extends a primer until a dideoxynucleotide (ddNTP) is incorporated, causing termination. Four separate reactions generate DNA fragments ending at A, T, G, or C, which are then resolved using electrophoresis.
Key Points:
- High accuracy and long read lengths (up to ~1000 bp)
- Labor-intensive but reliable
- Widely used for genome projects and clinical validations
- Maxam-Gilbert Sequencing (Chemical Cleavage Method)
Introduced by Maxam and Gilbert, this method involves chemically modifying and cleaving DNA at specific bases (adenine, guanine, cytosine, thymine). Radioactively or fluorescently labeled DNA fragments are treated with chemicals and separated on a gel to deduce the sequence.
Key Points:
- Does not require DNA polymerase
- Useful for double-stranded DNA templates
- Complex, hazardous (uses toxic chemicals), and difficult to automate
- Pyrosequencing (Real-Time PPi Detection Method)
Pyrosequencing detects the release of pyrophosphate (PPi) during nucleotide incorporation by DNA polymerase. This PPi triggers a cascade of enzymatic reactions, producing light proportional to the number of nucleotides added. Light intensity is measured in real-time to determine the sequence.
Key Points:
- Rapid and real-time detection
- No need for labeled primers or gels
- Best suited for short-to-medium reads (typically up to ~200 bases)
- Limited by difficulties in reading long homopolymer stretches
- Single-Molecule Sequencing with Exonuclease
Single-molecule sequencing represents a real-time approach in which individual DNA strands are sequenced without the need for amplification. Early techniques, such as exonuclease-based methods using fluorescently labeled nucleotides, showed initial promise by detecting each base as it was cleaved. However, these approaches have largely been replaced by more scalable and advanced platforms like PacBio and Oxford Nanopore, which offer longer reads, higher throughput, and improved accuracy.
Key Points:
- Enables sequencing of large DNA fragments (40 kb or more)
- Real-time sequencing at high speeds (up to 100–1000 bases per second)
- Requires sensitive detection systems and robust base labeling
- Early exonuclease-based methods are now largely outdated and have been replaced by PacBio/Nanopore
Below is the comparison table that gives a comparative overview of each DNA sequencing method’s principle, advantages, limitations, and applications.
| Technique | Principle | Advantages | Limitations | Applications |
| Sanger Sequencing | Chain termination by ddNTP incorporation during enzymatic DNA synthesis | High accuracy, long read lengths (~1000 bp), and reliable | Labor-intensive, lower throughput, expensive for large genomes | Genome sequencing, clinical diagnostics, and mutation validation |
| Maxam-Gilbert Sequencing | Chemical cleavage at specific DNA bases (A, G, C, T) followed by gel electrophoresis | Works on double-stranded DNA, no polymerase needed | Hazardous chemicals, complex procedures, and difficult automation | Early genomic mapping, template validation |
| Pyrosequencing | Real-time detection of pyrophosphate (PPi) release during DNA synthesis | Fast, real-time, no need for gels or labels | Short read lengths (~30–100 bp), trouble with homopolymers | SNP detection, mutation analysis, short sequence reads |
| Single-Molecule Sequencing | Detection of individual fluorescent nucleotides released by exonuclease digestion | Very high speed (100–1000 bases/sec), large fragment potential | Experimental stage, needs sensitive detection systems, labeling complexity | Future high-speed sequencing, large genome analysis |
Traditional DNA sequencing methods have each contributed uniquely to advancing molecular biology. While Sanger sequencing remains the benchmark for high accuracy, chemical methods like Maxam-Gilbert provided alternatives in the early days.
Innovations like pyrosequencing and single-molecule techniques set the foundation for real-time, high-throughput sequencing. Now, let’s explore the traditional RNA sequencing techniques.
Traditional RNA Sequencing Techniques
RNA sequencing has evolved significantly over the years, starting from early biochemical methods to the advanced next-generation sequencing (NGS) technologies we use today. Traditional RNA sequencing methods laid the foundation for understanding gene expression and the complexities of the transcriptome.
Although somewhat limited by modern standards, these methods provided essential insights into RNA biology and paved the way for high-throughput technologies like RNA-Seq. Below are some of the significant traditional RNA sequencing techniques:
- Expressed Sequence Tag (EST) Method
The Expressed Sequence Tag (EST) method, developed in the early 1990s, was among the earliest large-scale techniques to catalog expressed genes. EST involves partial sequencing of cDNA clones, which are reverse-transcribed from mRNA. The resulting sequences, or tags, serve as a snapshot of the genes being actively expressed during RNA extraction.
- Serial Analysis of Gene Expression (SAGE)
Developed by Velculescu et al. in the 1990s, the Serial Analysis of Gene Expression (SAGE) method improved upon the EST approach by significantly reducing the cost of sequencing. Instead of sequencing entire cDNA clones, SAGE sequences short tags (15-21 bp) from cDNA molecules. These tags, which represent the gene of origin, are concatenated and sequenced.
- DNA Microarrays
By the mid-1990s, DNA microarrays became the dominant high-throughput gene expression analysis method. This technology allows for the simultaneous detection of thousands of RNA transcripts. Microarrays use pre-designed probes to hybridize with labeled cDNA, which is synthesized from RNA samples.
Below is the comparison table that gives a comparative overview of each RNA sequencing method’s principle, advantages, limitations, and applications.
| Technique | Principle | Advantages | Limitations | Applications |
| Expressed Sequence Tag (EST) | Partial sequencing of cDNA clones to reveal gene expression and RNA abundance. | Great for gene discovery and provides initial insights into gene expression. | High sequencing cost, semi-quantitative, limited for large-scale studies. | Gene discovery, initial gene expression exploration. |
| Serial Analysis of Gene Expression (SAGE) | Sequencing short tags (15-21 bp) from cDNA, representing gene expression. | Lower sequencing cost compared to EST, and high-throughput expression analysis. | Limited to short tags, semi-quantitative, decreased specificity. | Gene expression profiling, large-scale expression analysis. |
| DNA Microarrays | Hybridization of labeled RNA with probes attached to a solid surface. | High-throughput, affordable for large-scale studies, genome-wide analysis. | Requires known sequence information, cross-hybridization issues, and low sensitivity. | Genome-wide expression profiling, disease research. |
| Next-Generation RNA Sequencing (RNA-Seq) | High-throughput sequencing of cDNA to directly measure RNA levels and sequence. | High accuracy, ability to identify novel RNA species, no prior sequence info needed. | Higher cost than microarrays, requires bioinformatics for data analysis. | Gene expression analysis, alternative splicing detection, novel RNA discovery. |
While these traditional RNA sequencing techniques, such as EST, SAGE, and DNA microarrays, were groundbreaking at the time, they have been largely replaced by next-generation sequencing methods like RNA-Seq. These newer technologies have overcome the limitations of traditional methods by offering higher accuracy, greater sensitivity, and the ability to identify novel RNA species and alternative transcript isoforms.
To better understand how modern genomic analysis has evolved, it’s essential to explore the innovations driving speed and scale, particularly high-throughput sequencing technologies.
High-Throughput Sequencing Technologies
Source: Pixabay
Over the past few decades, advancements in high-throughput sequencing (HTS) technologies have drastically transformed the field of genomics. Next-generation sequencing (NGS) platforms have played a central role among these. The completion of the human genome in 2001, achieved using the traditional Sanger sequencing method, was a major milestone, but that method was time-consuming, expensive, and limited in output.
The demand for faster, more cost-effective sequencing led to the rise of HTS methods like NGS, which can sequence billions of DNA fragments in parallel. This innovation has significantly lowered costs and increased speed, opening the door to personalized genomics, clinical diagnostics, and deeper insights into biological systems that were once out of reach.
Popular HTS Platforms
- Illumina: Illumina is the most widely used HTS platform. It uses a step-by-step process to read DNA with high accuracy. Devices like MiSeq, HiSeq, and NextSeq support projects of different sizes. Though the reads are short, it works well for genome sequencing, gene expression studies, and targeted sequencing.
- Ion Torrent: Ion Torrent technology detects changes in pH during DNA synthesis, skipping the need for light-based detection. It is fast and cost-effective, especially for smaller genomes and targeted sequencing. However, it struggles with long reads and makes more errors in certain DNA regions.
- Pacific Biosciences (SMRT): PacBio’s SMRT sequencing reads long DNA fragments, aiding in genome assembly and the detection of structural variations. It operates without the need for amplification, though it comes at a higher cost and may initially have higher error rates. PacBio improves accuracy using circular consensus sequencing (CCS), which involves repeatedly reading the same DNA molecule.
- Oxford Nanopore: Oxford Nanopore sequencing passes DNA through tiny pores and reads electrical signals to determine the sequence. It supports extremely long reads and is portable, making it well-suited for fieldwork and real-time analysis. Although error rates are higher than those of other platforms, Nanopore sequencing is continually improving and has found applications in infectious disease diagnostics and real-time outbreak tracking.
Applications of HTS
- Genome Sequencing: HTS allows fast, affordable genome sequencing, helping researchers study rare diseases, human variation, and population genetics.
- Transcriptomics: RNA sequencing (RNA-Seq) shows which genes are active and how they are used. It helps discover new RNAs and track changes in gene expression.
- Epigenomics: HTS maps chemical changes to DNA that affect gene activity. It helps researchers understand gene regulation and how epigenetic changes influence health.
- Clinical Diagnostics: HTS supports medical testing, such as cancer gene analysis, prenatal screening, and disease mutation detection. It’s a key tool in personalized medicine.
As HTS technologies evolve, new approaches such as single-molecule and real-time sequencing are pushing the boundaries of what is possible in genomics research and clinical practice. Let’s explore this sequencing technology.
Single-Molecule and Real-Time Sequencing
Source: NIH – Overview of SMRT Sequencing Technology
The development of DNA sequencing has seen major improvements, especially with the rise of single-molecule sequencing platforms. These technologies help overcome the limitations of earlier high-throughput sequencing (HTS), such as short read lengths and difficulty analyzing complex areas of the genome.
Platforms like SMRT (Single-Molecule Real-Time) sequencing by Pacific Biosciences (PacBio) and nanopore sequencing by Oxford Nanopore Technologies now offer longer reads and better resolution of complex genomic regions. These technologies enhance structural variant detection and enable direct epigenetic analysis, with improved accuracy achieved through repeated reading techniques such as circular consensus sequencing (CCS).
What Is Single-Molecule Sequencing?
DNA sequencing started with Sanger sequencing, which was reliable but slow and expensive. Next came second-generation sequencing, or NGS, which allowed many DNA fragments to be sequenced at once. However, NGS still had issues with short reads and couldn’t easily analyze repetitive or complex regions of DNA.
Third-generation sequencing solved many of these problems by reading single DNA molecules directly, without needing amplification. This avoids errors caused by amplifying the DNA. Technologies like PacBio’s SMRT sequencing and Oxford Nanopore’s nanopore sequencing can read very long DNA fragments, making them better suited for studying full genes, complex structural variations, and epigenetic changes.
How SMRT Sequencing Works
PacBio’s SMRT sequencing uses a special setup called a SMRT Cell, which contains tiny observation chambers known as Zero Mode Waveguides (ZMWs). Each chamber holds a single DNA molecule. As a polymerase enzyme reads the DNA, it adds fluorescently labeled nucleotides one by one. A camera records the light signal from each addition, revealing the DNA sequence in real time.
One of SMRT’s strengths is its ability to read the same DNA strand multiple times, creating what’s known as a Circular Consensus Sequence (CCS). This increases accuracy and helps avoid errors common in other technologies.
Clinical Uses of SMRT Sequencing
SMRT sequencing is being used in many areas of medicine:
- Genetic Testing: It helps identify complex or structural variants that short-read technologies may miss, improving diagnostic accuracy in rare genetic disorders.
- Reproductive Health: SMRT is used in prenatal and pre-implantation testing to detect mutations or chromosomal problems early in development.
- Cancer Research: It can identify large genetic changes and gene fusions in tumors. These findings are key to understanding drug resistance and tumor behavior.
- Microbial and Viral Genomics: SMRT can fully sequence bacterial and viral genomes, helping to identify new strains and detect drug-resistance mutations.
While SMRT sequencing has advanced the field through its highly accurate long reads, another groundbreaking technology, nanopore sequencing, offers additional advantages such as portability, ultra-long reads, and real-time data streaming. Together, these platforms represent the forefront of third-generation sequencing innovations. Below, you will get an in-depth look at nanopore sequencing.
Nanopore Sequencing
Nanopore sequencing is a direct method for reading long strands of DNA or RNA without the need for amplification or labeling. It works by pulling nucleic acid molecules through a tiny nanopore using voltage, detecting each base based on changes in electrical current.
This approach offers a faster, simpler, and more affordable alternative to traditional methods like Sanger and next-generation sequencing. First demonstrated in 2001 using protein pores, this technology laid the groundwork for the modern commercial nanopore platforms used today.
How It Works
A voltage is applied across a nanopore, and a single nucleic acid strand is pulled through. As the strand moves through the pore, different bases (A, T, C, G) create unique disruptions in the ionic current. This signal is then decoded to reveal the nucleotide sequence. Adapter molecules are used to guide the strand through smoothly.
Key Advantages
- Long Read Lengths: One of the most significant advantages of nanopore sequencing is its ability to produce extremely long reads, which can exceed 100 kb in length. This is especially beneficial for sequencing complex genomic regions, repetitive elements, and long DNA fragments that are difficult to assemble using short-read technologies.
- Minimal Sample Preparation: Nanopore sequencing often requires minimal sample preparation for DNA, though RNA workflows may involve reverse transcription to cDNA depending on the method.
- Real-Time Sequencing: Nanopore sequencing allows for real-time data acquisition, enabling immediate analysis of sequencing data. This rapid turnaround time is particularly useful in clinical diagnostics, where timely results are crucial.
- Direct Detection of Epigenetic Modifications: Nanopore sequencing has the potential to directly detect epigenetic modifications, such as DNA methylation, without the need for additional chemical treatments or labeling. This feature opens up new possibilities for studying gene regulation and other epigenetic processes.
- Cost-Effective: Requires fewer reagents and equipment, reducing overall sequencing costs. While costs vary by experiment and platform, Nanopore’s simplified setup and fewer reagent requirements can significantly reduce sequencing expenses.
Nanopore technology is rapidly evolving and is especially valued for fieldwork, diagnostics, and quick-turnaround sequencing tasks.
Future of Sequencing
Source: NIH – DNA Sequencing Technologies on Human Genome
The future of DNA sequencing is full of exciting possibilities that could transform healthcare, diagnostics, and research. As technology improves, sequencing is expected to become faster, cheaper, and more useful across many fields. Here’s a look at where things are headed:
- Longer Reads, Higher Accuracy
New methods like PacBio and Oxford Nanopore are improving read lengths and accuracy. Techniques like PacBio HiFi now offer long reads with high accuracy, useful for detecting structural variants and resolving complex regions of the genome.
- Real-Time Sequencing
Devices like the MinION already offer real-time sequencing, meaning data is available as DNA is being read. This is helpful for quick medical decisions, tracking disease outbreaks, and field research.
- Lower Costs, Greater Access
Experts predict that sequencing a whole genome could soon cost under $100, making it more accessible for research and clinical use.
- Combining Sequencing with Other Data (Multi-Omics)
Future research will mix sequencing with other biological data like proteins and chemicals in the body. This complete picture will help scientists understand how genes work together with other systems in the body.
- AI and Machine Learning
With so much genetic data, AI and machine learning will be used to speed up analysis and improve accuracy. This helps in making better predictions and diagnoses from the data.
- Everyday Clinical Use
In the future, DNA sequencing will be a regular part of medical care. It will help diagnose diseases, guide treatments, and even prevent illnesses by spotting genetic risks early.
Conclusion
The future of sequencing is defined by speed, precision, and accessibility. With advanced platforms like PacBio and Oxford Nanopore, long-read and real-time sequencing are now making it possible to explore the genome more deeply and accurately than ever before. As these technologies become more affordable and reliable, DNA and RNA sequencing are taking centre stage in diagnostics, personalized medicine, and cutting-edge research.
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FAQs
1. Which Next-Generation Sequencing Method Is Best for DNA Sequencing?
The best NGS method depends on the application. Illumina platforms are widely favored for their high accuracy and cost-effectiveness in short-read sequencing. For long-read and structural variant analysis, PacBio HiFi and Oxford Nanopore are preferred.
2. How Can You Combine or Integrate Multiple RNA-Seq Datasets?
To integrate RNA-seq datasets, normalize for batch effects using tools like ComBat, Seurat, or Harmony. Use consistent preprocessing steps and reference annotations across datasets. Integrated analysis enables comparison across conditions, platforms, or experiments.
3. What are the four methods of DNA sequencing?
The four primary DNA sequencing methods are Sanger sequencing, which uses chain-terminating nucleotides for accuracy; Maxam-Gilbert sequencing, which uses chemical cleavage; Pyrosequencing, detecting pyrophosphate release for real-time sequencing; and Next-Generation Sequencing (NGS), which provides massively parallel sequencing for faster, more cost-effective results in genomic research.
4. What Is the Recommended Sequencing Depth for ChIP-Seq and RNA-Seq?
ChIP-seq typically requires ~20–50 million reads per sample, depending on the target and antibody quality. RNA-seq depth varies: ~10–30 million reads for gene expression, and >50 million for detecting splice variants or low-abundance transcripts. Higher depth increases resolution, especially for complex analyses.