Accurate somatic small variant discovery for multiple sequencing technologies with DeepSomatic.

Document Type

Article

Publication Date

9-2026

Identifier

DOI: 10.1038/s41587-025-02839-x

Abstract

Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies offer potential advantages in repeat mapping and variant phasing. We present DeepSomatic, a deep-learning method for detecting somatic small nucleotide variations and insertions and deletions from both short-read and long-read data. The method has modes for whole-genome and whole-exome sequencing and can run on tumor-normal, tumor-only and formalin-fixed paraffin-embedded samples. To train DeepSomatic and help address the dearth of publicly available training and benchmarking data for somatic variant detection, we generated and make openly available the Cancer Standards Long-read Evaluation (CASTLE) dataset of six matched tumor-normal cell line pairs whole-genome sequenced with Illumina, PacBio HiFi and Oxford Nanopore Technologies, along with benchmark variant sets. Across samples, both cell line and patient-derived, and across short-read and long-read sequencing technologies, DeepSomatic consistently outperforms existing callers.

Journal Title

Nature biotechnology

Volume

44

Issue

9

First Page

1569

Last Page

1578

MeSH Keywords

Humans; Neoplasms; High-Throughput Nucleotide Sequencing; Sequence Analysis, DNA; Deep Learning; Genetic Variation; Exome Sequencing; Cell Line, Tumor; Whole Genome Sequencing; Genomics

PubMed ID

41102444

Keywords

Neoplasms; High-Throughput Nucleotide Sequencing; DNA Sequence Analysis; Deep Learning; Genetic Variation; Exome Sequencing; Tumor Cell Line; Whole Genome Sequencing; Genomics

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