Maize Genomics and Genetics 2024, Vol.15, No.5, 218-227 http://cropscipublisher.com/index.php/mgg 219 2 Methodological Approaches in Phylogenomics 2.1 Genomic data acquisition In phylogenomic studies of the genus Zea, various types of genomic data are utilized, including whole genome sequences and transcriptomic data. Whole genome sequencing, such as the sequencing of complete plastid genomes (plastomes), provides comprehensive data that can be used to analyze microstructural changes like inversions and indels (Orton et al., 2017) Transcriptomics, which involves sequencing RNA to study gene expression, is another valuable source of data. For instance, RNA sequencing has been used to assemble large and accurate phylogenomic datasets, as demonstrated in studies of jawed vertebrates (Irisarri et al., 2017). The methods for collecting and curating genomic data are diverse. Whole genome sequencing can be performed using next-generation sequencing technologies, which allow for the sequencing of entire genomes at a relatively low cost (Allio et al., 2019). Transcriptomic data can be obtained through RNA sequencing, which involves extracting RNA, converting it to cDNA, and then sequencing the cDNA (Irisarri et al., 2017). Data curation involves filtering and assembling the raw sequence data to ensure accuracy and completeness. For example, in the study of swallowtail butterflies, orthologous coding sequences were identified from whole-genome shotgun sequences, and these sequences were then used for phylogenomic analyses (Allio et al., 2019). 2.2 Phylogenomic analysis techniques Phylogenomic analysis involves several bioinformatics tools and techniques for alignment and phylogenetic tree construction. Tools such as IQ-TREE and PhyloBayes are commonly used for maximum-likelihood and Bayesian mixture model analyses, respectively (Allio et al., 2019). These tools help in constructing phylogenetic trees by aligning sequences and estimating evolutionary relationships based on the aligned data. Models of molecular evolution are crucial in phylogenomic analyses. These models describe how sequences evolve over time and are used to infer phylogenetic relationships. For instance, the noncorrelated relaxed clock method is used to estimate divergence times by allowing different parts of the genome to evolve at different rates (Orton et al., 2017). Other models, such as the uncorrelated lognormal (UCLN) model, are used to account for rate heterogeneity across genes and lineages (Smith et al., 2018). 2.3 Addressing methodological challenges Phylogenomic studies face several methodological challenges, including incomplete lineage sorting and hybridization. Incomplete lineage sorting occurs when the gene tree does not match the species tree due to ancestral polymorphisms. This issue can be addressed using multispecies coalescent methods, which consider the coalescent process of gene lineages within species (Koenen et al., 2019). Hybridization, which involves the mixing of genetic material from different species, can confound phylogenetic analyses. Techniques such as the D-statistic (ABBA-BABA test) are used to detect introgression and hybridization events (Vargas et al., 2017). Managing large genomic datasets is another significant challenge. Phylogenomic datasets can be vast, making it difficult to perform analyses on the entire dataset. One approach to manage this issue is "gene shopping," where a subset of genes with desirable properties (e.g., clock-likeness, reasonable tree length, and minimal topological conflict) is selected for analysis (Smith et al., 2018). This method helps reduce errors associated with model mis-specification and makes divergence-time estimation more efficient. 3 Evolutionary Relationships withinZea 3.1 Phylogenetic trees of Zea species The construction and interpretation of phylogenetic trees are fundamental to understanding the evolutionary relationships within the genus Zea. Phylogenetic trees are graphical representations that depict the evolutionary pathways and relationships among different species based on genetic data. In the context of Zea, complete plastid genomes (plastomes) have been utilized to construct these trees, providing insights into the microstructural changes such as inversions and insertion or deletion mutations (indels) that have occurred over time (Orton et al., 2017).
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