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Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Understanding the evolutionary relationships among microorganisms is fundamental to microbial ecology and taxonomy. Phylogenetic trees are essential tools for inferring these relationships, relying primarily on comparative analyses of molecular sequences such as DNA, RNA, or proteins. In microbial studies, these trees typically depict the evolutionary paths of diverse bacterial and archaeal species by mapping genetic differences accumulated over time.Phylogenetic trees are composed of tips,...
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Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.
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Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
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Understanding tissue expression evolution: from expression phylogeny to phylogenetic network.

Xun Gu

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    PubMed
    Summary
    This summary is machine-generated.

    High-throughput technologies advance understanding of tissue expression evolution. A phylogenetic network approach offers insights into developmental similarity and evolutionary relatedness across tissues.

    Keywords:
    expression phylogenyphylogenetic networktissue expression evolutiontissue-driven hypothesis

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    Area of Science:

    • Evolutionary biology
    • Genomics
    • Developmental biology

    Background:

    • High-throughput technologies like RNA-seq have significantly advanced the study of tissue expression evolution in multicellular organisms.
    • Despite progress, significant controversies persist regarding evolutionary patterns and phylogenetic analysis of tissue expression.
    • A unified framework for studying tissue expression evolution is currently lacking.

    Purpose of the Study:

    • To provide a comprehensive review of current research on tissue expression evolution in multicellular organisms.
    • To explore the utility of phylogenetic network approaches in understanding multi-tissue evolution.
    • To address unresolved questions concerning evolutionary patterns and phylogenetic analysis of tissue expression.

    Main Methods:

    • Review of existing literature on tissue expression evolution.
    • Analysis of expression phylogenies using data from closely and intermediately related species.
    • Application of phylogenetic network methods to study multi-tissue evolution.

    Main Results:

    • Expression phylogenies of homologous tissues generally mirror species phylogenies.
    • Phylogenetic network approaches can illuminate developmental similarities and evolutionary relatedness across multiple tissues.
    • The study highlights the complexity and nuances in understanding tissue expression evolution.

    Conclusions:

    • While species phylogeny is reflected in tissue expression, a broader evolutionary picture emerges with network approaches.
    • Phylogenetic networks offer a promising framework for dissecting the complexities of multi-tissue evolutionary dynamics.
    • Further research integrating network approaches is crucial for a complete understanding of tissue expression evolution.