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Related Concept Videos

Bootstrapping01:24

Bootstrapping

The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is small or...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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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,...
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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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Increasing data transparency and estimating phylogenetic uncertainty in supertrees: Approaches using nonparametric

Brian R Moore1, Stephen A Smith, Michael J Donoghue

  • 1Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut 06520, USA. brian.moore@yale.edu

Systematic Biology
|September 15, 2006
PubMed
Summary

Estimating large phylogenies is challenging. New bootstrapping methods improve supertree accuracy and quantify uncertainty by incorporating character data, enhancing phylogenetic inference transparency.

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Area of Science:

  • Computational Biology
  • Phylogenetics
  • Bioinformatics

Background:

  • Estimating large phylogenies necessitates advanced inference strategies like divide-and-conquer approaches.
  • Supertree methods combine component topologies but often overlook underlying character data and phylogenetic uncertainty.

Purpose of the Study:

  • To explore novel bootstrapping techniques for supertree estimation.
  • To enhance the transparency of supertrees to character data and quantify topological uncertainty.

Main Methods:

  • Incorporation of nonparametric bootstrapping into matrix representation with parsimony (MRP).
  • Exploration of three bootstrapping approaches: bootstrap-weighting, source-tree bootstrapping, and hierarchical bootstrapping.
  • Implementation in the freely available program, tREeBOOT.

Main Results:

  • Preliminary experiments indicate improved correspondence between supertree and simultaneous analysis estimates.
  • Demonstrated potential for quantifying uncertainty in supertree topologies.
  • Increased transparency of supertrees to underlying character data.

Conclusions:

  • Bootstrapping methods offer a promising avenue for more robust and interpretable supertree construction.
  • These approaches address limitations of traditional supertree methods by integrating character data and uncertainty assessment.
  • The tREeBOOT program provides a practical tool for applying these advanced phylogenetic inference techniques.