Somatic variants are changes in the DNA that occur after conception, and they have been linked not only to cancer, but also to a range of developmental, aging-related, and neurological disorders. While many tools have been developed to find these mutations using DNA data, they often fall short when applied to RNA sequencing data, especially in healthy tissues where mutation rates are low. To address this gap, a team led by researchers at Boston Children’s Hospital have developed a new computational tool called RNA-MosaicHunter that can accurately detect somatic single-nucleotide variants from standard bulk RNA-seq data.

RNA-MosaicHunter works by carefully analyzing RNA sequencing datasets to find rare genetic changes that traditional DNA-based tools might miss or misinterpret. In benchmark tests, the tool showed high precision when identifying true somatic variants in both cancer and mixed cell line datasets, meaning it was able to correctly identify real mutations while minimizing false positives. When applied to large collections of RNA-seq samples from the Genotype Tissue Expression (GTEx) project, RNA-MosaicHunter outperformed previous methods in capturing age-related mutational patterns across tissues.

Importantly, the team used the tool to analyze RNA-seq data from hundreds of Alzheimer’s disease brain samples and age-matched controls. They found that sSNVs were significantly more abundant in the cerebral cortex of individuals with Alzheimer’s, suggesting that somatic mutations detectable through RNA sequencing might contribute to disease processes in the aging brain. By enabling accurate profiling of somatic variants across tissues, RNA-MosaicHunter opens the door to new discoveries about how post-birth mutations influence health and disease.