MOTIVATION: Extended tandem repeats (TRs) have been associated with 60 or more diseases over the past 30 years. Although most TRs have single repeat units (or motifs), complex TRs with different units have recently been correlated with some brain disorders. Of note, a population-scale analysis shows that complex TRs at one locus can be divergent, and different units are often expanded between individuals. To understand the evolution of high TR diversity, it is informative to visualize a phylogenetic tree. To do this, we need to measure the edit distance between pairs of complex TRs by considering duplication and contraction of units created by replication slippage. However, traditional rigorous algorithms for this purpose are computationally expensive. RESULTS: We here propose an efficient heuristic algorithm to estimate the edit distance with duplication and contraction of units (EDDC, for short). We select a set of frequent units that occur in given complex TRs, encode each unit as a single symbol, compress a TR into an optimal series of unit symbols that partially matches the original TR with the minimum Levenshtein distance, and estimate the EDDC between a pair of complex TRs from their compressed forms. Using substantial synthetic benchmark datasets, we demonstrate that the estimated EDDC is highly correlated with the accurate EDDC, with a Pearson correlation coefficient of >0.983, while the heuristic algorithm achieves orders of magnitude performance speedup. AVAILABILITY AND IMPLEMENTATION: The software program hEDDC that implements the proposed algorithm is available at https://github.com/Ricky-pon/hEDDC (DOI: 10.5281/zenodo.14732958).

MOTIVATION: Over the past 30 years, extended tandem repeats (TRs) have been correlated with ∼60 diseases with high odds ratios, and most known TRs consist of single repeat units. However, in the last few years, mosaic TRs composed of different units have been found to be associated with several brain disorders by long-read sequencing techniques. Mosaic TRs are difficult-to-characterize sequence configurations that are usually confirmed by manual inspection. Widely used tools are not designed to solve the mosaic TR problem and often fail to properly decompose mosaic TRs. RESULTS: We propose an efficient algorithm that can decompose mosaic TRs in the input string with high sensitivity. Using synthetic benchmark data, we demonstrate that our program named uTR outperforms TRF and RepeatMasker in terms of prediction accuracy, this is especially true when mosaic TRs are more complex, and uTR is faster than TRF and RepeatMasker in most cases. AVAILABILITY AND IMPLEMENTATION: The software program uTR that implements the proposed algorithm is available at https://github.com/morisUtokyo/uTR.