Computational evolutionary analyses, particularly phylogenetics and ancestral reconstruction, have been extensively exploited under different algorithms and evolutionary models to better understand genome evolution from both small- and large-scale perspectives in order to assign genotypes based on assortment, resolve species relationships and gene annotation issues, further understand gene gain/loss within individual gene families, measure functional divergence among homologs, and infer ancestral character states. These evolutionary studies provide us with insights into biologically relevant issues including paleoenvironments inferred from resurrected proteins, developmental physiology associated with functional divergence of duplicated genes, viral epidemics and modes of transmission in attempt to better prepare, prevent and control diseases, evolution of lineage-specific pathogenicity, and attempts to create a synthetic ancient organism that would benefit the field of synthetic biology. Our work also provides us with greater insights into the accuracies and limitations of ancestral sequence reconstruction methods. In total, our work highlights the diverse questions that evolutionary studies attempt to address and the different biological levels that can be studied to answer these questions.
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Exploiting phylogenetics to understand genome evolution in both modern and ancestral organisms
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