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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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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Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.
Speciation Rates01:07

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
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Efficient selection of branch-specific models of sequence evolution.

Julien Y Dutheil1, Nicolas Galtier, Jonathan Romiguier

  • 1Institut des Sciences de l'Évolution-Montpellier, Université Montpellier 2, Montpellier, France. julien.dutheil@univ-montp2.fr

Molecular Biology and Evolution
|February 10, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational method to identify varying molecular evolution patterns across phylogenetic trees without prior assumptions. The approach effectively clusters evolutionary rates, revealing insights into sequence and species evolution, such as relaxed selection in frogs.

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Area of Science:

  • Molecular Biology
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Molecular evolution rates vary significantly across lineages and over time.
  • Identifying the drivers of this heterogeneity is challenging due to limitations in existing methods.
  • Current approaches require either prior assumptions about evolutionary patterns or excessive parameter estimation.

Purpose of the Study:

  • To develop a novel, data-driven method for clustering phylogenetic branches based on their molecular evolution patterns.
  • To overcome the limitations of existing methods by avoiding a priori group definitions and overparameterization.
  • To provide a reliable tool for uncovering hidden patterns and correlations in sequence and species evolution.

Main Methods:

  • A statistical framework is employed for model selection, avoiding overparameterization.
  • Substitution mapping is utilized for computational efficiency.
  • Two clustering approaches are proposed: one assuming similarity between neighboring branches and one without this assumption.
  • The method is validated using simulations and applied to real biological datasets.

Main Results:

  • The method successfully groups branches with similar equilibrium GC content in ribosomal RNA data.
  • It accurately identifies signatures of selection using the dN/dS ratio.
  • A novel pattern of relaxed selection was discovered in Mantellid frogs, linked to life-history traits.

Conclusions:

  • The developed method offers a convenient and reliable way to analyze heterogeneous molecular evolution.
  • It facilitates the discovery of new correlations between molecular sequence evolution and species evolution.
  • The software is versatile, supporting various sequence types and substitution models, and is publicly available.