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Related Concept Videos

Phylogenetic Trees03:21

Phylogenetic Trees

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

Phylogenetic Trees

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...
Microbial Phylogeny01:28

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,...
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...
Phylogeny01:23

Phylogeny

Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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.
In contrast, regions which code...

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A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

A practical algorithm for reconstructing level-1 phylogenetic networks.

Katharina T Huber1, Leo van Iersel, Steven Kelk

  • 1School of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, UK. Katharina.Huber@cmp.uea.ac.uk

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|March 12, 2011
PubMed
Summary
This summary is machine-generated.

We developed LEV1ATHAN, an efficient algorithm for constructing level-1 phylogenetic networks from evolutionary data. It accurately reconstructs complex evolutionary histories, even with noisy data.

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Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Phylogenetic trees are limited in representing complex evolutionary histories.
  • Phylogenetic networks generalize trees to model processes like hybridization and recombination.
  • Reconstructing phylogenetic networks from data is computationally challenging.

Purpose of the Study:

  • To present an efficient and practical algorithm for reconstructing level-1 phylogenetic networks.
  • To provide a publicly available software tool (LEV1ATHAN) for network reconstruction.
  • To assess the performance of the algorithm on simulated and biological data.

Main Methods:

  • Developed a novel algorithm combining existing theoretical approaches with new subroutines.
  • Implemented an exact exponential-time algorithm and a greedy polynomial-time algorithm.
  • Utilized triplet data as input for network reconstruction.

Main Results:

  • LEV1ATHAN runs in polynomial time and always produces a level-1 network.
  • The algorithm reconstructs phylogenetic trees when data are consistent with them.
  • LEV1ATHAN successfully identifies level-1 networks from dense, consistent triplet data.
  • The program demonstrates high accuracy in constructing networks from noisy triplet data.

Conclusions:

  • LEV1ATHAN is an effective tool for inferring complex evolutionary relationships.
  • The algorithm performs well even with moderate levels of noise in the input data.
  • LEV1ATHAN advances the field of phylogenetic network reconstruction.