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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...
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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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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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Updated: Jul 4, 2026

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Fixed-parameter algorithms in phylogenetics.

Jens Gramm1, Arfst Nickelsen, Till Tantau

  • 1Wilhelm-Schickard-Institut für Informatik, Universität Tübingen, Tübingen, Germany.

Methods in Molecular Biology (Clifton, N.J.)
|June 21, 2008
PubMed
Summary

Fixed-parameter algorithms offer efficient solutions for complex phylogenetic tree construction problems. These computational methods are crucial for accurately inferring evolutionary relationships from genetic and phenotypic data.

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Last Updated: Jul 4, 2026

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Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Phylogenetics involves constructing genealogical trees to understand species evolution.
  • Key challenges include inferring trees from phenotypic, genotypic, or partial data.
  • Perfect phylogenies are ideal but often unattainable; approximations are necessary.

Purpose of the Study:

  • To survey the application of fixed-parameter algorithms in solving phylogenetic problems.
  • To highlight the utility of these algorithms for computationally intensive tasks in phylogenetics.

Main Methods:

  • Review of fixed-parameter algorithm applications in phylogenetics.
  • Discussion of computational problems in phylogeny construction, including NP-complete problems.
  • Exploration of parametrizations amenable to fixed-parameter tractability.

Main Results:

  • Fixed-parameter algorithms can effectively solve many NP-complete problems in phylogenetics.
  • These algorithms provide a powerful approach for constructing phylogenies when perfect trees are not feasible.
  • The methods are applicable to diverse areas, including genomic sequencing and gene analysis.

Conclusions:

  • Fixed-parameter algorithms are a vital tool for advancing phylogenetic inference.
  • Their application enables more accurate and efficient construction of evolutionary trees.
  • This approach addresses the computational complexity inherent in many phylogenetics tasks.