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

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 l

SUBMITTER: Chen L 

PROVIDER: S-EPMC12579226 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature