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Phylogenetic Trees03:21

Phylogenetic Trees

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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.
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Survival Tree01:19

Survival Tree

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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.
 Building a Survival Tree
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Phylogeny01:23

Phylogeny

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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.
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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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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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Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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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.
In contrast, regions which code...
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The Tree of Life - Bacteria, Archaea, Eukaryotes02:40

The Tree of Life - Bacteria, Archaea, Eukaryotes

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The “tree of life” describes the evolution of life and the evolutionary relationships between organisms. The root of the tree is the common ancestor to all life on Earth. All other species radiate from this point, much like the branches of a tree. The numerous tips of these branches on the tree of life represent every living, or extant, species. Extinct species, which are species that no longer exist, can be found towards the center of the tree. Currently, these organisms, both...
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Related Experiment Video

Updated: Oct 20, 2025

A Practical Guide to Phylogenetics for Nonexperts
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A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

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Improved Fixed-Parameter Algorithm for the Tree Containment Problem on Unrooted Phylogenetic Network.

Feng Shi, Hangcheng Li, Guozhen Rong

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |September 10, 2021
    PubMed
    Summary
    This summary is machine-generated.

    Phylogenetic networks model complex evolutionary histories. This study introduces a faster algorithm for the unrooted Tree Containment problem, improving computational efficiency for phylogenetic analysis.

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    Area of Science:

    • Computational Biology
    • Evolutionary Biology
    • Bioinformatics

    Background:

    • Phylogenetic trees cannot represent reticulate evolution.
    • Phylogenetic networks generalize evolutionary models to include reticulation events.
    • The Tree Containment problem determines if a phylogenetic tree is contained within a phylogenetic network.

    Purpose of the Study:

    • To address the computational challenge of the unrooted Tree Containment problem.
    • To improve upon existing fixed-parameter algorithms for unrooted phylogenetic networks.
    • To develop a more efficient algorithm for analyzing gene evolution within species.

    Main Methods:

    • Developed a novel fixed-parameter algorithm for the unrooted Tree Containment problem.
    • The algorithm achieves a runtime complexity of O(2.594^k * n^2).
    • Experimental validation was performed on both simulated and real biological data.

    Main Results:

    • The proposed algorithm significantly improves upon the previous O(4^k * n^2) runtime.
    • Demonstrated the algorithm's effectiveness and practicality on diverse datasets.
    • Provided a more efficient computational tool for phylogenetic network analysis.

    Conclusions:

    • The new algorithm offers a substantial speedup for the unrooted Tree Containment problem.
    • This advancement facilitates more accurate reconstruction of evolutionary histories, especially for genes within complex species.
    • The improved computational efficiency is crucial for analyzing large-scale phylogenetic data.