What is deep sequencing (Next-Generation Sequencing, NGS)?

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Last updated: April 1, 2025 • View editorial policy

From the Guidelines

Deep sequencing, also known as next-generation sequencing (NGS), is the most effective method for detecting genetic mutations in non-small cell lung cancer, due to its high sensitivity and ability to detect rare genetic variants, somatic mutations, and low-frequency alleles 1.

Introduction to Deep Sequencing

Deep sequencing is a powerful genomic technology that allows for the simultaneous analysis of millions to billions of DNA fragments. This technique provides a comprehensive view of genetic material by sequencing DNA at much higher depth than traditional methods, typically generating hundreds to thousands of reads per targeted region.

Advantages of Deep Sequencing

Deep sequencing is particularly valuable for detecting rare genetic variants, somatic mutations, and low-frequency alleles that might be missed by conventional sequencing approaches. The process involves fragmenting DNA, preparing libraries with adaptors, amplifying these fragments, and then sequencing them using platforms such as Illumina, Ion Torrent, or Pacific Biosciences.

Applications of Deep Sequencing

This technology has revolutionized many fields including cancer genomics, microbial identification, metagenomics, and personalized medicine. The high throughput nature of deep sequencing enables researchers and clinicians to identify genetic variations with greater sensitivity and accuracy, making it essential for understanding complex genetic diseases and developing targeted therapies.

Limitations and Future Directions

However, it requires sophisticated bioinformatics tools to analyze the large datasets generated and interpret the biological significance of identified variants. According to a study published in the Journal of Thoracic Oncology, high-depth NGS can detect known and new mutations with a sensitivity of 1%–10% depending on error rates and sequencing depth 1.

Clinical Recommendations

The use of deep sequencing is recommended for patients with non-small cell lung cancer, as it can help identify genetic mutations that may be targeted with specific therapies, improving patient outcomes and quality of life 2. Some key points to consider when using deep sequencing in clinical practice include:

  • The need for high-quality DNA samples, which can be obtained from tissue or plasma specimens
  • The importance of using validated bioinformatics tools to analyze the large datasets generated
  • The potential for deep sequencing to detect rare genetic variants and low-frequency alleles that may be missed by conventional sequencing approaches
  • The need for careful interpretation of the results, taking into account the clinical context and the potential for false positives or false negatives.

From the Research

Deep Sequencing Overview

  • Deep sequencing, also known as next-generation sequencing, high-throughput sequencing, and massively parallel sequencing, is a technique used to sequence large amounts of DNA or RNA 3.
  • This technique can be used to sequence whole genomes, specific parts of the genome, or RNA expression 3.
  • Deep sequencing has various applications, including identifying genetic diseases, detecting changes in RNA structure, and measuring RNA expression [(3, 4)].

Applications of Deep Sequencing

  • Deep sequencing can be used to identify epigenetic events, such as changes in DNA methylation and RNA expression, which can reveal the history and impact of environmental exposures 3.
  • In oncology, next-generation sequencing can be used for genetic diagnosis, risk prediction, and cancer classification 4.
  • Deep sequencing can also be used to sequence large numbers of human genomes, providing high-quality data for clinical use 5.

Technical Aspects of Deep Sequencing

  • Deep sequencing involves various technical aspects, such as the length and depth of sequence reads, mapping and assembly of reads, sequencing error, and haplotypes 3.
  • A deep learning model can be used to predict next-generation sequencing depth from DNA sequence, which can help improve sequencing costs and sensitivities 6.

Ethical Considerations

  • Deep sequencing raises ethical dilemmas and practical challenges in research and clinical care, such as the decision to report incidental findings 3.
  • The use of deep sequencing in genetic diagnosis and therapy also raises ethical considerations, such as the potential for genetic discrimination and the need for informed consent 7.

References

Guideline

non-small cell lung cancer, version 6.2015.

Journal of the National Comprehensive Cancer Network : JNCCN, 2015

Research

Making sense of deep sequencing.

The international journal of neuropsychopharmacology, 2014

Research

Deep sequencing of 10,000 human genomes.

Proceedings of the National Academy of Sciences of the United States of America, 2016

Research

Genetic Disease and Therapy.

Annual review of pathology, 2021

Professional Medical Disclaimer

This information is intended for healthcare professionals. Any medical decision-making should rely on clinical judgment and independently verified information. The content provided herein does not replace professional discretion and should be considered supplementary to established clinical guidelines. Healthcare providers should verify all information against primary literature and current practice standards before application in patient care. Dr.Oracle assumes no liability for clinical decisions based on this content.