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Cancers Originate from Somatic Mutations in a Single Cell

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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

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Published on: October 18, 2013

ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling.

Lei Chen¹, Zhenxian Zheng², Junhao Su¹

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

Nature Communications

|November 1, 2025

Summary

Accurate somatic variant detection in tumors is crucial. ClairS-TO, a new deep learning tool, enables reliable tumor-only somatic variant calling from long-read sequencing data, improving cancer research and clinical applications.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Accurate somatic variant detection is critical for cancer research and clinical applications.
Existing methods often require matched normal samples, which are not always available.
Current tumor-only callers struggle with long-read sequencing data.

Purpose of the Study:

To develop a deep-learning-based method for long-read tumor-only somatic variant calling.
To address the limitations of existing algorithms in scenarios lacking matched normal samples.
To provide a reliable tool for somatic variant discovery in diverse cancer research settings.

Main Methods:

Developed ClairS-TO, a deep learning model utilizing an ensemble of two neural networks.
Trained networks on cancer cell line data (COLO829, HCC1395) for somatic variant identification.
Evaluated performance on both long-read (ONT, PacBio) and short-read sequencing data.

Main Results:

ClairS-TO demonstrated superior performance compared to existing methods (DeepSomatic, smrest) on long-read data.
ClairS-TO also outperformed established short-read callers (Mutect2, Octopus, Pisces, DeepSomatic).
The tool showed reliability across various sequencing coverages, variant allelic fractions, and tumor purities.

Conclusions:

ClairS-TO is a robust and accurate tool for tumor-only somatic variant calling, particularly for long-read data.
The method effectively distinguishes somatic variants from germline variants and artifacts without matched normal samples.
ClairS-TO offers a valuable open-source solution for advancing cancer genomics.