Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Evolutionary Relationships through Genome Comparisons

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...

Phylogenetic Trees

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.

Gene Evolution - Fast or Slow?

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...

Phylogeny

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 kingdom.

Genetics of Speciation

Genetics of Speciation

Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.

Probability Laws

Probability Laws

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Genetic affinities between the ancient Greek colony of Amvrakia and its metropolis.

Genome biology·2026

Same author

A systematic exploration of current limitations of cognate-based phylogenetic inference.

Open research Europe·2026

Same author

Bit-reproducible parallel phylogenetic tree inference.

Bioinformatics (Oxford, England)·2026

Same author

Performance assessment of phylogenetic inference tools using PhyloSmew.

Bioinformatics advances·2025

Same author

raxtax: a k-mer-based non-Bayesian taxonomic classifier.

Bioinformatics (Oxford, England)·2025

Same author

Advances and challenges in understanding evolution through genome comparison: meeting report of the European Molecular Biology Organization (EMBO) lecture course "Evolutionary and Comparative Genomics".

Bioinformatics advances·2025

Search research articles

Related Experiment Video

Updated: Jul 29, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Phylourny: efficiently calculating elimination tournament win probabilities via phylogenetic methods.

Ben Bettisworth¹, Alexander I Jordan², Alexandros Stamatakis^1,3,4

¹Heidelberg, Germany Computational Molecular Evolution, Heidelberg Institute for Theoretical Studies.

Statistics and Computing

|May 22, 2023

Summary

This study introduces a novel, highly efficient method for calculating exact knockout tournament win probabilities, drawing parallels with molecular evolution computations. The new approach significantly outperforms simulations and naive calculations, enabling advanced prediction strategies.

Keywords:

MCMC search Phylogenetic analysis Sports forecasting Uncertainty analysis

More Related Videos

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Related Experiment Videos

Last Updated: Jul 29, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Area of Science:

Computational Biology
Sports Analytics
Algorithm Development

Background:

Predicting knockout tournament outcomes is a significant challenge with broad interest.
Existing methods often rely on computationally intensive simulations or approximations.

Purpose of the Study:

To develop an exact and computationally efficient method for calculating per-team win probabilities in knockout tournaments.
To leverage analogies from phylogenetic likelihood calculations in molecular evolution for tournament prediction.

Main Methods:

Adapted phylogenetic likelihood score computation for tournament bracket analysis.
Implemented an open-source algorithm for exact win probability calculation.
Compared computational efficiency against simulation-based and naive methods.

Main Results:

The novel method is two orders of magnitude faster than simulations.
It is also two or more orders of magnitude faster than naive exact calculations.
Demonstrated feasibility of calculating 100,000 win probabilities for a 16-team tournament in under a minute.

Conclusions:

The developed method offers substantial computational savings for tournament prediction.
Enables novel prediction approaches and uncertainty quantification.
Provides a scalable solution applicable to tournaments of various sizes (e.g., 16 and 64 teams).