Mokveld, T.; Dolzhenko, E.; Dashnow, H.; Nicholas, T. J.; Sasani, T.; van der Sanden, B.; Jadhav, B.; Pedersen, B.; Kronenberg, Z.; Tucci, A.; Sharp, A. J.; Quinlan, A. R.; Gilissen, C.; Hoischen, A.; Eberle, M. A.

Motivation Identifying de novo tandem repeat (TR) mutations on a genome-wide scale is essential for understanding genetic variability and its implications in rare diseases. While PacBio HiFi sequencing data enhances the accessibility of the genome\'s TR regions for genotyping, simple de novo calling strategies often generate an excess of likely false positives, which can obscure true positive findings, particularly as the number of surveyed genomic regions increases. Results We developed TRGT-denovo, a computational method designed to accurately identify all types of de novo TR mutations-including expansions, contractions, and compositional changes-within family trios. TRGT-denovo directly interrogates read evidence, allowing for the detection of subtle variations often overlooked in variant call format (VCF) files. TRGT-denovo improves the precision and specificity of de novo mutation (DNM) identification, reducing the number of de novo candidates by an order of magnitude compared to genotype-based approaches. In our experiments involving eight rare disease trios previously studied, TRGT-denovo correctly reclassified all false positive DNM candidates as true negatives. Using an expanded repeat catalog, it identified new candidates, of which 95% (19/20) were experimentally validated, demonstrating its effectiveness in minimizing likely false positives while maintaining high sensitivity for true discoveries.