International Journal of Molecular Evolution and Biodiversity 2024, Vol.14, No.5, 219-228 http://ecoevopublisher.com/index.php/ijmeb 221 Figure 1 Sequence coverages in the 15 ulvophycean chloroplast genomes compared in this study (Adopted from Turmel et al., 2017) Image caption: (a) Sizes of the SSC, IR and LSC regions. Red arrows indicate the direction of transcription of the rRNA operon in IR-containing genomes. Genomes lacking the IR are represented in grey. The names of the newly examined taxa are indicated in red. (b) Amounts of coding, intronic, intergenic and small repeated sequences (≥30bp). Note that intron-encoded genes were not considered as coding sequences but rather as intron sequences (Adopted from Turmel et al., 2017) 3.2 Phylogenetic analysis techniques Phylogenetic analysis was conducted to understand the evolutionary relationships and divergence patterns within the chloroplast genomes. Multiple phylogenetic methods were employed, including parsimony analysis, which was sensitive to taxon sampling and could be computationally intensive with large datasets. Moreover, high-throughput sequencing data was used to analyze the phylogenetic signals of different genomic regions, including the complete chloroplast genome (Vargas et al., 2017; Qing et al., 2021). This approach helps in resolving phylogenetic incongruences that may arise due to hybridization and introgression events (Zhong et al., 2022). 3.3 Statistical methods for divergence detection Statistical methods were applied to detect divergence patterns and identified mutation hotspots within the chloroplast genome. It identified heterogeneous sequence divergence patterns in different regions of the chloroplast genomes, with a significant number of SNPs located in gene regions and indels distributed in intergenic spacers (Wang et al., 2018). To further validate these findings, the D-statistic (ABBA-BABA test) was used to detect introgression and hybridization events, which could confound phylogenetic analyses (Vargas et al., 2017). These statistical tools are essential for accurately detecting and interpreting divergence patterns in the chloroplast genome. By integrating these methods, it’s able to identify and characterize the divergence patterns in the chloroplast genome of Eucommia ulmoides, providing valuable insights for future conservation genomics studies (Fučíková et al., 2016).
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