Tandem repeat sequences present challenges in genome assembly due to the difficulty in resolving their repetitive units and the uncertainty surrounding their copy numbers. During sequence alignment, the high similarity between repeated units and the ambiguity in copy numbers often result in multiple candidate positions. The challenge lies in rapidly and accurately filtering out the correct alignment positions. Existing methods address this issue by using seeds (short sequences selected from sequencing fragments) to locate and extend candidate alignment positions. However, these methods overlook the distinctive characteristics of tandem repeat sequences when selecting seeds. To tackle the problem, a position filtering method for tandem repeat sequence alignment is proposed, which filters alignment results by calculating the similarity of rare kmer sequences. Additionally, a strategy of merging rare kmers is implemented to expedite computation, coupled with a fuzzy search based on edit distance to enhance filtering information density. Experimental results demonstrate that this approach improves both the recall and accuracy of alignment results on simulated datasets while achieving approximately a 2-fold increase in computational speed compared to existing methods, with notable parallel acceleration effects.

[2024-03-07] Accepted Paper /doc/2023.12.0614/v1