A curated benchmark dataset for molecular identification based on genome skimming
Abstract Genome skimming is a promising sequencing strategy for DNA-based taxonomic identification. However, the lack of standardized datasets for benchmarking genome skimming tools presents a challenge in comparing new methods to existing ones. As part of the development of varKoder, a new tool for...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05230-2 |
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| Summary: | Abstract Genome skimming is a promising sequencing strategy for DNA-based taxonomic identification. However, the lack of standardized datasets for benchmarking genome skimming tools presents a challenge in comparing new methods to existing ones. As part of the development of varKoder, a new tool for DNA-based identification, we curated four datasets designed for comparing molecular identification tools using low-coverage genomes. These datasets comprise vast phylogenetic and taxonomic diversity from closely related species to all taxa currently represented on NCBI SRA. One of them consists of novel sequences from taxonomically verified samples in the plant clade Malpighiales, while the other three datasets compile publicly available data. All include raw genome skim sequences to enable comprehensive testing and validation of a variety molecular species identification methods. We also provide the two-dimensional graphical representations of genomic data used in varKoder. These datasets represent a reliable resource for researchers to assess the accuracy, efficiency, and robustness of new tools to varKoder and other methods in a consistent and reproducible manner. |
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| ISSN: | 2052-4463 |