ycf1-ndhF genes, the most promising plastid genomic barcode, sheds light on phylogeny at low taxonomic levels in Prunus persica.

BACKGROUND Chloroplast genome sequencing is becoming a valuable process for developing several DNA barcodes. At present, plastid DNA barcode for systematics and evolution in flowering plant rely heavily on the use of non-coding genes. The present study was performed to verify the novelty and suitability of the two hotspot barcode plastid coding gene ycf1 and ndhF, to estimate the rate of molecular evolution in the Prunus genus at low taxonomic levels. RESULTS Here, 25 chloroplast genomes of Prunus genus were selected for sequences annotation to search for the highly variable coding DNA barcode regions. Among them, 5 genera were of our own data, including the ornamental, cultivated, and wild haplotype, while 20 genera have been downloaded from the GenBank database. The results indicated that the two hotspot plastid gene ycf1 and ndhF were the most variable regions within the coding genes in Prunus with an average of 3268 to 3416 bp in length, which have been predicted to have the highest nucleotide diversity, with the overall transition/transversion bias (R = 1.06). The ycf1-ndhF structural domains showed a positive trend evident in structure variation among the 25 specimens tested, due to the variant overlap's gene annotation and insertion or deletion with a broad trend of the full form of IGS sequence. As a result, the principal component analysis (PCA) and the ML tree data drew an accurate monophyletic annotations cluster in Prunus species, offering unambiguous identification without overlapping groups between peach, almond, and cherry. CONCLUSION To this end, we put forward the domain of the two-locus ycf1-ndhF genes as the most promising coding plastid DNA barcode in P. persica at low taxonomic levels. We believe that the discovering of further variable loci with high evolutionary rates is extremely useful and potential uses as a DNA barcode in P. persica for further phylogeny study and species identification.