ArticleBMC bioinformatics2021
Reliable genomic strategies for species classification of plant genetic resources.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Novel genomic regions associated with adult-plant resistance to multiple fungal pathogens in wheat (Triticum aestivum L.) revealed by DArT marker sequencing.Journal of applied genetics · 2026Article
- Unraveling the variation, phylogeny, and taxonomy of Lactuca spp. for germplasm management and breeding.Planta · 2025Review
- AI-driven advances in plant biotechnology: sharpening the edge of plant tissue culture and genome editing.Frontiers in plant science · 2025Review
- Accurate, automated taxonomic assignment of genebank accessions: a new method demonstrated using high-throughput marker data from 10,000 Capsicum spp. accessions.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Thin-Layer Chromatography (TLC) in the Screening of Botanicals-Its Versatile Potential and Selected Applications.Molecules (Basel, Switzerland) · 2022Review
- Application of crop wild relatives in modern breeding: An overview of resources, experimental and computational methodologies.Frontiers in plant science · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundTo address the need for easy and reliable species classification in plant genetic resources collections, we assessed the potential of five classifiers (Random Forest, Neighbour-Joining, 1-Nearest Neighbour, a conservative variety of 3-Nearest Neighbours and Naive Bayes) We investigated the effects of the number of accessions per species and misclassification rate on classification success, and validated theirs generic value results with three complete datasets.
resultsWe found the conservative variety of 3-Nearest Neighbours to be the most reliable classifier when varying species representation and misclassification rate. Through the analysis of the three complete datasets, this finding showed generic value. Additionally, we present various options for marker selection for classification taks such as these.
conclusionsLarge-scale genomic data are increasingly being produced for genetic resources collections. These data are useful to address species classification issues regarding crop wild relatives, and improve genebank documentation. Implementation of a classification method that can improve the quality of bad datasets without gold standard training data is considered an innovative and efficient method to improve gene bank documentation.
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Registered trials
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