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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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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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Related Experiment Video

Updated: Jun 29, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Pangenomes at the limits of evolution.

Joanna M Wolfe1

  • 1Museum of Comparative Zoology, Harvard University, Cambridge, MA 02138, USA; Department of Organismic & Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA.

Trends in Ecology & Evolution
|April 5, 2024
PubMed
Summary

Evolutionary pathways are not entirely random. Machine learning applied to microbial pangenomes reveals that the presence of many genes can be predicted, suggesting deterministic elements in evolution.

Area of Science:

  • Microbial genomics
  • Evolutionary biology
  • Computational biology

Background:

  • Evolutionary pathways are shaped by both random (stochastic) and deterministic processes.
  • Understanding the balance between these forces is crucial for predicting evolutionary trajectories.

Purpose of the Study:

  • To investigate the predictability of gene presence in microbial pangenomes.
  • To quantify the balance between random and deterministic factors in microbial evolution using machine learning.

Main Methods:

  • Application of machine learning models to analyze microbial pangenome data.
  • Inference of gene presence and absence patterns across microbial species.

Main Results:

  • Machine learning models reliably predicted the presence of nearly one-third of genes within microbial pangenomes.

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  • This finding indicates a significant degree of determinism in microbial genome evolution.

    • Conclusions:

    • Microbial evolution exhibits a higher degree of predictability than previously assumed.
    • The study highlights the power of machine learning in uncovering deterministic patterns in biological systems.