Minimum virtual dataset for reproducible triploid de novo genome assembly

Benchmark datasets for assessing polyploid genome assembly are limited because real polyploid genomes often contain unknown structural variations, complex repeats, and heterogeneous divergence among homologous copies. In this study, a minimal, fully controlled virtual dataset is provided for reproducible benchmarking of triploid de novo assembly using short reads. A 1-Mbp haploid reference sequence is generated and iteratively mutated to produce three genome copies (A-C) across 100 mutation steps, creating a divergence gradient that transitions from nearly identical to moderately diverged triploid genomes. For each divergence level, paired-end Illumina reads are simulated at uniform coverage and processed through error correction followed by de Bruijn graph assembly across multiple k-mer sizes. The dataset provides the full set of reference genomes, read sets, assemblies, and evaluation metrics, allowing direct reproduction of trends such as overcollapsed contigs at low divergence and improved genome separability at higher divergence. This compact resource offers method developers and users a transparent, reproducible standard for evaluating k-mer strategies and assembly behavior in triploid genomes.