A dataset for benchmarking molecular identification tools based on genome skimming

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Abstract

Genome skimming is an emerging tool allowing for scalable DNA barcoding efforts for numerous biodiversity science applications. Despite its growing importance, there are few standardized datasets for benchmarking genome skimming tools, making it challenging to evaluate new methods (e.g., using machine learning), and comparing to existing ones (e.g., conventional barcoding loci derived from Sanger-based sequencing). To address this gap, we present four curated datasets designed for benchmarking 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 four datasets compile publicly available data. All include raw genome skim sequences and two-dimensional graphical representations of genomic data (chaos game representations and varKodes), enabling comprehensive testing and validation of molecular species identification methods. These datasets represent a reliable resource for researchers to assess the accuracy, efficiency, and robustness of their tools in a consistent and reproducible manner.
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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. DOI https://doi.org/10.32942/X2DW6K Subjects Biodiversity, Bioinformatics, Ecology and Evolutionary Biology, Genetics and Genomics, Life Sciences

Keywords

barcoding, varKoder, biodiversity Dates Published: 2024-12-19 16:13 Last Updated: 2025-06-03 14:43 Older Versions License CC-BY Attribution-NonCommercial 4.0 International Additional Metadata Conflict of interest statement: CCD declares that he is supported by LVMH Research and Dior Science, a company involved in the research and development of cosmetic products based on floral extracts. He also serves as a member of Dior’s Age Reverse Board. Language: English

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License: CC-BY-NC-4.0