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Somatic mutation detection: a critical evaluation through simulations and reanalyses in oaks

Mutation, the source of genetic diversity, is the raw material of evolution; however, the mutation process remains understudied, especially in plants. Using both a simulation and reanalysis framework, we set out to explore and demonstrate the improved performance of variant callers developed for cancer research compared to single nucleotide polymorphism (SNP) callers in detecting de novo somatic mutations. In an in silico experiment, we generated Illumina-like sequence reads spiked with simulated mutations at different allelic fractions to compare the performance of seven commonly-used variant callers to recall them. More empirically, we then reanalyzed two of the largest datasets available for plants, both developed for identifying within-individual variation in long-lived pedunculate oaks. Based on the in silico experiment, variant callers developed for cancer research outperform SNP callers regarding plant mutation recall and precision, especially at low allele frequency. Such variants at low allelic fractions are typically expected for within-individual de novo plant mutations, which initially appear in single cells. Reanalysis of published oak data with Strelka2, the best-performing caller based on our simulations, identified up to 3.4x more candidate somatic mutations than reported in the original studies. Our results advocate the use of cancer research callers to boost de novo mutation research in plants, and to reconcile empirical reports with theoretical expectations. This version of the article has been peer-reviewed and recommended by Peer Community in Genomics https://doi.org/10.24072/pci.genomics.100024