ArticleNature communications2025
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Telomere-to-telomere CHM13 reference reveals missing truth variants and improves deep learning-based variant calling in long-read sequencing data.Quantitative biology (Beijing, China) · 2026Article
- Somatic mosaicism in the brain: linking development, ageing and neurodegeneration.Nature reviews. Neurology · 2026Review
- Nano-RAPID: Integrated Diagnosis for Sarcoma Using a Nanopore Sequencer.Genes, chromosomes & cancer · 2026Article
- Somatic likelihood tiering: an interpretable post-calling triage protocol for tumor-only whole-exome variant review.Briefings in bioinformatics · 2026Article
- 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
- Pansoma, a machine learning tool for identifying somatic variants using pangenome graphs.bioRxiv : the preprint server for biology · 2026Article
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Improved tumor-only variant calling and mutation burden estimation with VarNet-T.Nature communications · 2026Article
- Article
- Satellite DNA fragility accompanies complex genome rearrangements and ecDNA oncogene amplification in canine osteosarcomas.bioRxiv : the preprint server for biology · 2025Article
- Evaluation of Long-Read Genome Sequencing for Genomic Profiling of Myeloid Cancers.The Journal of molecular diagnostics : JMD · 2025Article
- Bioinformatics frameworks for single-cell long-read sequencing: unlocking isoform-level resolution.Briefings in bioinformatics · 2025Review
- ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling.Nature communications · 2025Article
- Comprehensive benchmarking of somatic single-nucleotide variant and indel detection at ultra-low allele fractions using short- and long-read data.bioRxiv : the preprint server for biology · 2025Article
- Characteristics and filtering of low-frequency artificial short deletion variations based on nanopore sequencing.GigaScience · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate detection of somatic variants in tumors is of critical importance and remains challenging. Current methods typically require matched normal samples for reliable detection, which are often unavailable in real-world research and clinical scenarios. Without a matched normal sample, more proficient algorithms are required to distinguish true somatic variants from germline variants and technical artifacts. However, existing tumor-only somatic variant callers that were designed for short-read sequencing data are not able to work well with long-read data. To fill the gap, we present ClairS-TO, a deep-learning-based method for long-read tumor-only somatic variant calling. ClairS-TO uses an ensemble of two disparate neural networks trained from the same samples but for opposite tasks-how likely/not likely a candidate is a somatic variant. Benchmarks using COLO829 and HCC1395 cancer cell lines show that ClairS-TO outperforms DeepSomatic and smrest in ONT and PacBio long-read data. ClairS-TO is also applicable to short-read data and outperforms Mutect2, Octopus, Pisces, and DeepSomatic. Extensive experiments across various sequencing coverages, variant allelic fractions, and tumor purities support that ClairS-TO is a reliable tool for somatic variant discovery. ClairS-TO is open-source, available at https://github.com/HKU-BAL/ClairS-TO .
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