Unravelling the Chloroplast Genome of the Kazakh Apricot (Prunus armeniaca L.) Through MinION Long-Read Sequencing

The study of the genetic diversity and adaptation mechanisms of the Kazakh apricot (<i>Prunus armeniaca</i> L.) is essential for breeding programs and the conservation of plant genetic resources in arid environments. Despite this species' ecological and agricultural significance, its chloroplast genome remains poorly studied due to its complex repetitive structure and secondary metabolites that hinder high-molecular-weight DNA (HMW-DNA) extraction and long-read sequencing. To address this gap, our study aims to develop and optimise sequencing protocols for <i>P. armeniaca</i> under arid conditions using Oxford Nanopore's MinION technology. We successfully extracted HMW-DNA with 50-100 ng/μL concentrations and purity (A260/A280) between 1.8 and 2.0, ensuring high sequencing quality. A total of 10 GB of sequencing data was generated, comprising 155,046 reads, of which 74,733 (48.2%) had a Q-score ≥ 8. The average read length was 1679 bp, with a maximum of 31,144 bp. Chloroplast genome assembly resulted in 33,000 contigs with a total length of 1.1 Gb and a BUSCO completeness score of 97.3%. Functional annotation revealed key genes (nalC, AcrE, and mecC-type BlaZ) associated with stress tolerance and a substantial proportion (≈40%) of hypothetical proteins requiring further investigation. GC content analysis (40.25%) and GC skew data suggest the presence of specific regulatory elements linked to environmental adaptation. This study demonstrates the feasibility of using third-generation sequencing technologies to analyse complex plant genomes and highlights the genetic resilience of <i>P. armeniaca</i> to extreme conditions. The findings provide a foundation for breeding programs to improve drought tolerance and conservation strategies to protect Kazakhstan's unique arid ecosystems.