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Related Experiment Video

Updated: Sep 22, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Embedding gene trees into phylogenetic networks by conflict resolution algorithms.

Marcin Wawerka¹, Dawid Dąbkowski², Natalia Rutecka²

¹University of Warsaw, Faculty of Mathematics, Informatics and Mechanics, Banacha 2, 02-097, Warsaw, Poland. marcin.wawerka@gmail.com.

Algorithms for Molecular Biology : AMB

|May 19, 2022

Summary

This study introduces efficient algorithms for inferring optimal displayed trees from phylogenetic networks, crucial for understanding evolutionary histories shaped by reticulate events like hybridization.

Keywords:

Deep coalescence Gene tree Optimal displayed tree Phylogenetic network Reticulation Species tree Tree-child network

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Last Updated: Sep 22, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

Published on: July 11, 2025

Area of Science:

Evolutionary biology
Computational phylogenetics
Bioinformatics

Background:

Phylogenetic networks model evolutionary processes with reticulate events (hybridization, recombination, HGT).
Displayed trees are derived from networks by removing reticulation edges.
Displayed trees can represent gene family evolution shaped by reticulations.

Purpose of the Study:

To develop algorithms for inferring optimal displayed trees from gene trees and tree-child networks.
To compute lower bounds for displayed tree costs under deep coalescence and duplication costs.
To provide methods for verifying exact solutions and resolving conflicting reticulation edges.

Main Methods:

A dynamic programming (DP) algorithm with O(mn) time complexity for computing lower bounds.
A conflict resolution algorithm utilizing DP invocations.
Algorithms for level-k tree-child networks and branch and bound solutions.
Extension of algorithms to broader classes of phylogenetic networks.

Main Results:

An O(mn) DP algorithm computes a lower bound for optimal displayed tree cost and verifies exactness.
A conflict resolution algorithm is proposed, requiring O(r) DP calls.
Efficient algorithms for level-k networks and branch and bound solutions are presented.
Simulated data shows average runtimes of O(mn) for deep coalescence and O(mn) for duplication costs.

Conclusions:

Algorithms perform well on empirical and simulated data, outperforming enumeration strategies.
Efficiently resolve internal dissimilarities between gene trees and networks.
Enable analysis of complex phylogenetic networks with numerous reticulations.