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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Although all next-generation methods use different technologies, they all share a set of standard features.
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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...

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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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ClairS: a deep-learning method for long-read tumor-normal pair somatic small variant calling.

Zhenxian Zheng1, Lei Chen1, Junhao Su1

  • 1School of Computing and Data Science, The University of Hong Kong, Hong Kong, China.

Nature Methods
|July 1, 2026
PubMed
Summary
This summary is machine-generated.

Clair-Somatic (ClairS) is a new deep-learning tool for identifying somatic variants in long-read sequencing data from tumor samples. It accurately detects small variants, improving cancer analysis and clinical applications.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Somatic variant detection is vital for cancer clinical analysis.
  • Existing methods primarily focus on short-read sequencing, limiting long-read applications.
  • There is a need for specialized tools for somatic variant calling using long-read sequencing data.

Purpose of the Study:

  • To develop and evaluate Clair-Somatic (ClairS), a deep-learning-based somatic small-variant caller for long-read tumor-normal sequencing data.
  • To assess the performance of ClairS across various sequencing conditions and datasets.
  • To highlight the advantages of long-read sequencing for variant detection.

Main Methods:

  • Developed ClairS, a deep-learning model trained on synthetic and real somatic variants.
  • Utilized Nanopore Q20+ HCC1395-HCC1395BL dataset for performance evaluation.
  • Tested ClairS under diverse coverage, purity, and contamination levels across multiple platforms and real cancer cell lines.

Main Results:

  • ClairS achieved high F1 scores: 89.83% for single-nucleotide variations and 73.38% for indels on a specific dataset.
  • Augmenting training with real cancer cell lines improved performance to 96.19% for SNVs and 79.67% for indels.
  • Demonstrated robustness and reliability of ClairS across varied experimental conditions.

Conclusions:

  • Long-read sequencing, particularly improved read phasing, is crucial for accurate somatic single-nucleotide variation detection, especially at low variant allele fractions.
  • ClairS is a robust and reliable tool for somatic small-variant discovery using long-read sequencing.
  • The open-source availability of ClairS facilitates its adoption in cancer research and clinical settings.