Minimum virtual dataset for reproducible triploid de novo genome assembly
Ryo Ootsuki1,2
1Department of Natural Sciences, Faculty of Arts and Sciences, Komazawa University, 1-23-1 Komazawa, Setagaya-ku, Tokyo, 154-8525, Japan. ootsuki@komazawa-u.ac.jp.
Scientific Data
|February 7, 2026
Summary
A new virtual dataset aids triploid genome assembly benchmarking. This resource enables reproducible analysis of de novo assembly strategies for complex genomes using short reads.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Assessing polyploid genome assembly is challenging due to limited benchmark datasets.
- Real polyploid genomes exhibit complex structural variations, repeats, and divergence.
Purpose of the Study:
- To provide a controlled virtual dataset for reproducible benchmarking of triploid de novo assembly.
- To evaluate assembly behavior across varying divergence levels in triploid genomes.
Main Methods:
- Generated a 1-Mbp haploid reference sequence and created three mutated copies (A-C).
- Simulated paired-end Illumina reads across a divergence gradient.
- Performed error correction and de Bruijn graph assembly with multiple k-mer sizes.
Main Results:
- The dataset allows reproduction of trends like overcollapsed contigs at low divergence.
- Demonstrated improved genome separability with increased divergence.
- Provided a comprehensive resource including reference genomes, reads, assemblies, and metrics.
Conclusions:
- The virtual dataset offers a transparent and reproducible standard for evaluating assembly methods.
- Facilitates the assessment of k-mer strategies and assembly behavior in triploid genomes.
- Aids method developers and users in advancing polyploid genome assembly techniques.
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