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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.
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.
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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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Evolutionary Relationships through Genome Comparisons

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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 kingdom.
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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A Practical Guide to Phylogenetics for Nonexperts
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A mixed integer linear programming model to reconstruct phylogenies from single nucleotide polymorphism haplotypes

Daniele Catanzaro1, Ramamoorthi Ravi, Russell Schwartz

  • 1Graphes et Optimisation Mathématique (G,O,M,), Computer Science Department, Université Libre de Bruxelles (U,L,B,), Boulevard du Triomphe, CP 210/01, B-1050, Brussels, Belgium. dacatanz@ulb.ac.be.

Algorithms for Molecular Biology : AMB
|January 25, 2013
PubMed
Summary

This study introduces new methods to solve the Most Parsimonious Phylogeny Estimation Problem (MPPEP) for single nucleotide polymorphism haplotypes. These techniques improve computational efficiency and accuracy in phylogenetic analysis.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Evolutionary Genetics

Background:

  • Phylogeny estimation from haplotype sequences is crucial for genetic data analysis in medicine, drug discovery, epidemiology, and population dynamics.
  • The Most Parsimonious Phylogeny Estimation Problem (MPPEP) seeks the shortest phylogenetic tree, but is NP-hard for many versions.
  • Parsimony criteria require the shortest tree where path weights reflect observed genetic changes between haplotypes.

Purpose of the Study:

  • To investigate a recent version of MPPEP using single nucleotide polymorphism (SNP) haplotypes from population data.
  • To improve upon existing implicit enumeration strategies for MPPEP.
  • To develop novel problem formulations and constraints for more precise bounding and faster enumeration of optimal phylogenies.

Main Methods:

  • Developed a novel problem formulation for MPPEP with SNP haplotype data.
  • Introduced a series of strengthening valid inequalities and preliminary symmetry breaking constraints.
  • Applied these constraints to reduce the solution space and accelerate implicit enumeration.

Main Results:

  • Demonstrated significant reductions in the gap between optimal solutions and linear programming bounds.
  • Achieved substantially faster processing times for moderately hard problem instances compared to prior art.
  • Validated the effectiveness of the new constraints in bounding the solution space more precisely.

Conclusions:

  • The developed methodology is suitable for provably optimal solutions to challenging MPPEP instances.
  • These strategies are feasible for relatively large numbers of taxa, with limitations on the number of variable sites.
  • The approach offers a significant advancement for solving complex phylogenetic estimation problems.