ArticleFrontiers in plant science2024
A comprehensive evaluation of the potential of three next-generation short-read-based plant pan-genome construction strategies for the identification of novel non-reference sequence.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 4 citations in OpenAlex.
- The Rise of Plant Pan-Genomes: From Genome Variation to Predictive Breeding.Advanced genetics (Hoboken, N.J.) · 2026Article
- Application of FT-NIR spectroscopy to the prediction of Chromium contamination in soil by evolutionary chemometrics.PloS one · 2026Article
- Pan-genomics of polyploid crops: from complexity to breeding.Frontiers in plant science · 2026Review
- Current State of Fish Reference Genome and Pangenome: Methodologies, Sampling Strategies, Quality Assessment and Future Perspectives to Aquaculture Breeding.Marine biotechnology (New York, N.Y.) · 2025Review
- Current and future pangenomic research in cucurbit crops.Breeding science · 2025Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
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Abstract
Pan-genome studies are important for understanding plant evolution and guiding the breeding of crops by containing all genomic diversity of a certain species. Three short-read-based strategies for plant pan-genome construction include iterative individual, iteration pooling, and map-to-pan. Their performance is very different under various conditions, while comprehensive evaluations have yet to be conducted nowadays. Here, we evaluate the performance of these three pan-genome construction strategies for plants under different sequencing depths and sample sizes. Also, we indicate the influence of length and repeat content percentage of novel sequences on three pan-genome construction strategies. Besides, we compare the computational resource consumption among the three strategies. Our findings indicate that map-to-pan has the greatest recall but the lowest precision. In contrast, both two iterative strategies have superior precision but lower recall. Factors of sample numbers, novel sequence length, and the percentage of novel sequences' repeat content adversely affect the performance of all three strategies. Increased sequencing depth improves map-to-pan's performance, while not affecting the other two iterative strategies. For computational resource consumption, map-to-pan demands considerably more than the other two iterative strategies. Overall, the iterative strategy, especially the iterative pooling strategy, is optimal when the sequencing depth is less than 20X. Map-to-pan is preferable when the sequencing depth exceeds 20X despite its higher computational resource consumption.
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