A de novo genome assembly of cultivated Prunus persica cv. 'Sovetskiy'
Background: Prunus persica is a vital stone fruit crop in Crimea and southern Russia, yet existing genomic resources do not adequately represent the unique cultivars maintained at the Nikitsky Botanical Garden, which originate from diverse ecological and geographical groups across Central and East Asia. The 'Sovetskiy' cultivar, bred in the early 1950s by crossing 'Golden Jubilee' with the Armenian 'Narinji Late', is highly valued for its ecological plasticity, high productivity, and exceptional frost resistance in flower buds. This study provides a high-quality de novo genome assembly of 'Sovetskiy' to serve as a reference for identifying genes associated with these agriculturally significant traits and to assist in marker-assisted selection and comparative genomics within the Prunus genus.
Results: Using a hybrid assembly approach combining Oxford Nanopore long reads and Illumina short reads, the researchers generated a 206.26 Mb genome assembly distributed across 226 scaffolds with a N50 of 24 Mb, successfully anchoring the sequences into eight chromosomes. The assembly contains 27,140 predicted coding genes, with 99.38% annotated in at least one functional database, and identifies that approximately 36.05% of the genome consists of repetitive elements, primarily LTR-retrotransposons like Copia and Gypsy. Comparative analysis against the 'Lovell' reference genome revealed 467,207 SNPs and 37,930 indels, including 1,110 "damaged" genes that may be context-dependent or redundant under specific cultivation environments, providing a rich resource for understanding phenotypic and genomic variation.
Conclusions: The de novo assembly of the 'Sovetskiy' genome represents a significant advancement for peach breeding in southern Russia, offering a specialized reference for genotyping regional collections and identifying loci responsible for environmental resistance. Furthermore, the study's identification of damaged genes with context-dependent expression levels offers fundamental insights into how plant genomes adapt to artificial cultivation conditions. This genomic data serves as a fundamental basis for future genetic improvement, helping breeders create stone fruit cultivars that are increasingly resilient to both biotic and abiotic stressors.
https://pubmed.ncbi.nlm.nih.gov/35714114/
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