Lin, GuoliangHuang, ZhiruYue, TingsongChai, JingLi, YanYang, HuiminQin, WantingYang, GuobingMurphy, Robert W.Zhang, Ya-pingZhang, ZijieZhou, WeiLuo, Jing2025-08-142025-08-142024https://doi.org/10.1371/journal.pone.0298564https://hdl.handle.net/10012/22162© 2024 Lin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.High-quality, chromosome-scale genomes are essential for genomic analyses. Analyses, including 3D genomics, epigenetics, and comparative genomics rely on a high-quality genome assembly, which is often accomplished with the assistance of Hi-C data. Curation of genomes reveal that current Hi-C-assisted scaffolding algorithms either generate ordering and orientation errors or fail to assemble high-quality chromosome-level scaffolds. Here, we offer the software Puzzle Hi-C, which uses Hi-C reads to accurately assign contigs or scaffolds to chromosomes. Puzzle Hi-C uses the triangle region instead of the square region to count interactions in a Hi-C heatmap. This strategy dramatically diminishes scaffolding interference caused by long-range interactions. This software also introduces a dynamic, triangle window strategy during assembly. Initially small, the window expands with interactions to produce more effective clustering. Puzzle Hi-C outperforms available scaffolding tools.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/human genomicscomputer softwaregenomicsgenome analysischromosomesplant genomicsfish genomicsgenetic lociArticle