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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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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.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...
Phylogenetic Trees03:21

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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.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...
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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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Microbial Phylogeny

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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Synteny and Evolution

John H. Renwick first coined the term “synteny” in 1971, which refers to the genes present on the same chromosomes, even if they are not genetically linked. The species with common ancestry tend to show conserved syntenic regions. Therefore, the concept of synteny is nowadays used to describe the evolutionary relationship between species.
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Updated: Jun 22, 2026

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Data structures for parsimony correlation and biosequence co-evolution.

Robert Hochberg1, Treena Larrew Milam

  • 1Department of Computer Science, East Carolina University , Greenville, North Carolina.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|July 1, 2009
PubMed
Summary

This study introduces a novel algorithm for detecting co-evolution in biological sequences using phylogenetic data. The method efficiently computes correlations to reveal evolutionary relationships between genes.

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Last Updated: Jun 22, 2026

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Understanding co-evolution in biological sequences is crucial for deciphering functional relationships and evolutionary pathways.
  • Existing methods may face computational challenges with large datasets and complex phylogenetic relationships.

Purpose of the Study:

  • To develop an efficient algorithm for discovering co-evolutionary signals in aligned biosequences.
  • To enable rapid, interactive analysis of co-evolutionary patterns within a phylogenetic context.

Main Methods:

  • The algorithm correlates vectors of parsimony scores across edges of a graph.
  • It averages scores over optimally parsimonious reconstructions of the sequence data.
  • An efficient data structure and preprocessing step facilitate rapid correlation computations.

Main Results:

  • The developed method allows for the discovery of co-evolution in biosequences.
  • The approach enables interactive computation of numerous correlation scores.
  • This is achieved through an optimized data structure and preprocessing, trading storage for speed.

Conclusions:

  • The algorithm provides an efficient means to identify co-evolution in biosequences.
  • The method supports interactive exploration of evolutionary correlations.
  • This facilitates deeper insights into the functional and evolutionary interplay of biological sequences.