SomatiX: accurate and efficient somatic SNV detection from matched tumor-normal long-read DNA sequencing

Accurate somatic single-nucleotide variant (SNV) calling from matched tumor-normal long-read DNA sequencing remains challenging because sequencing errors, variable tumor purity, tumor-in-normal contamination, and low variant allele fractions (VAFs) can obscure tumor-specific signals. Although recent deep-learning methods have improved long-read somatic variant calling, robust low-VAF detection and efficient genome-scale analysis remain important methodological challenges.

We present SomatiX, a deep-learning method for somatic SNV detection from matched tumor-normal long-read DNA sequencing data, built on an allele-aware pileup representation. In comprehensive cancer benchmark analyses across diverse cell-line datasets, sequencing contexts, VAF bins, and minimum variant-supporting read thresholds, SomatiX outperformed DeepSomatic and ClairS in F1 score across most Oxford Nanopore Technologies (ONT) benchmark conditions and achieved competitive performance on Pacific Biosciences (PacBio) data. Across the held-out chromosome 1 cancer cell-line benchmarks, at the threshold of ≥ 3 variant-supporting reads and VAF ≥0.05, SomatiX achieved mean F1 scores of 0.986 for ONT data and 0.988 for PacBio data. SomatiX showed particularly strong performance for low-VAF SNVs in ONT data while using a compact model and achieving the fastest runtime among the evaluated tools.

SomatiX is available at https://github.com/colinliuzelin/SomatiX and archived at Zenodo under DOI: https://doi.org/10.5281/zenodo.22062438. The repository includes source code, trained models, and benchmarking scripts.

Supplementary data are available at Bioinformatics online.