In the star alignment algorithm for multiple sequence alignment, all sequences are aligned directly to the central’star’ sequence without using a guide tree. This method greatly reduces computation time, making star alignment-based tools effective for aligning homologous sequences with high similarity. However, as sequence similarity decreases or the number of sequences increases, the algorithm’s accuracy drops significantly. In particular, “junk sequences” with very low similarity to the central star sequence tend to result in poor alignments, which can degrade the overall alignment quality. While realignment methods can greatly enhance the accuracy of alignments, there is currently a lack of approaches specifically tailored for star alignment tools. This study presents ReAlign-Star, a realignment method specifically designed for star alignment-based tools. The core of ReAlign-Star employs two key strategies—filtering out “junk sequences” and applying local vertical partitioning for realignment—to efficiently improve the quality of star alignments. Experiments on both simulated and real datasets demonstrate that ReAlign-Star significantly improves alignment accuracy in most cases, outperforming the initial alignments and extending the applicability of star alignment tools. The source code and test data for ReAlign-Star are available on GitHub ( https://github.com/malabz/ReAlign-Star ).