ArticleNature methods2026
ClairS: a deep-learning method for long-read tumor-normal pair somatic small variant calling.
Article in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Somatic mosaicism in the brain: linking development, ageing and neurodegeneration.Nature reviews. Neurology · 2026Review
- nf-core/pacsomatic: a scalable somatic analytic pipeline using PacBio HiFi data.Bioinformatics (Oxford, England) · 2026Article
- ClairS: a deep-learning method for long-read tumor-normal pair somatic small variant calling.Nature methods · 2026Article
- A Personalized Haplotype-Resolved Near-Gapless Genome Framework for Somatic Variant Discovery in Hepatocellular Carcinoma.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Long-read sequencing of single cell-derived melanoma sublines reveals divergent and parallel genomic and epigenomic evolutionary trajectories.Nature communications · 2026Article
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Authors and funding
8 authors.
Funding
Abstract
Somatic variant discovery in tumors is crucial for clinical analysis, yet most existing methods are designed for short-read sequencing, with few developed specifically for long reads. Here we present Clair-Somatic (ClairS), a deep-learning-based somatic small-variant caller designed for long-read tumor-normal pairs. Trained on synthetic somatic variants with diverse coverages and variant allele fractions, ClairS accurately detects a wide range of somatic variants. Using the Nanopore Q20+ HCC1395-HCC1395BL dataset at 50/25× tumor/normal coverage, ClairS achieved F1 scores of 89.83% for single-nucleotide variations and 73.38% for indels; augmenting training with real cancer cell lines improved performance to 96.19% and 79.67%, respectively. Our findings indicate that improved read phasing enabled by long-read sequencing is key to accurate single-nucleotide variation detection, especially at low variant allele fractions. Through experiments across varied coverage, purity, contamination levels, multiple platforms and real cancer cell lines, we demonstrate that ClairS is a robust and reliable caller. ClairS is open source and available at https://github.com/HKU-BAL/ClairS .
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Registered trials
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