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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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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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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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Genetic Variation01:25

Genetic Variation

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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Gene Duplication and Divergence02:37

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The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
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Genetic transfer occurs when genetic information is passed from one organism to another. It occurs via two mechanisms: vertical gene transfer and horizontal gene transfer. Vertical gene transfer occurs when genetic information is transferred from one generation to the next, which happens much more frequently than horizontal gene transfer. Both sexual and asexual reproduction are forms of vertical gene transfer, where one or more organisms pass some or all of their genome onto their progeny.
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Related Experiment Video

Updated: May 12, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Generating Heterogeneous Data on Gene Trees.

Martí Cortada Garcia1, Adrià Diéguez Moscardó1, Marta Casanellas1,2

  • 1Department of Mathematics, Universitat Politècnica de Catalunya, Barcelona, Spain.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|May 9, 2025
PubMed
Summary
This summary is machine-generated.

GenPhylo is a new Python module for simulating nucleotide sequence data. It uses a general Markov model to naturally incorporate lineage heterogeneity, overcoming limitations of continuous-time Markov processes.

Keywords:
branch lengthnucleotide substitutionsequence data simulation

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Last Updated: May 12, 2025

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Area of Science:

  • Computational Biology
  • Phylogenetics
  • Bioinformatics

Background:

  • Phylogenetic analysis relies on simulating sequence evolution.
  • Existing methods often use continuous-time Markov processes, limiting heterogeneity.
  • Simulating sequence data with lineage-specific variation is challenging.

Purpose of the Study:

  • Introduce GenPhylo, a Python module for simulating nucleotide sequence data.
  • Overcome limitations of continuous-time Markov processes in phylogenetic simulations.
  • Enable the incorporation of heterogeneity across lineages in sequence data generation.

Main Methods:

  • GenPhylo directly employs a general Markov model.
  • The module avoids the restrictions of continuous-time Markov processes.
  • An algorithm is provided to generate transition matrices with a pre-defined expected number of substitutions.

Main Results:

  • GenPhylo successfully simulates nucleotide sequence data along phylogenies.
  • The simulation method naturally incorporates heterogeneity across different lineages.
  • The developed algorithm facilitates the generation of transition matrices for diverse simulation software.

Conclusions:

  • GenPhylo offers a flexible approach to phylogenetic sequence simulation.
  • The module enhances the realism of simulated evolutionary data by accounting for lineage heterogeneity.
  • GenPhylo provides a valuable tool for computational biology and phylogenetic research.