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

Search research articles

Related Experiment Videos

Using median sets for inferring phylogenetic trees.

Matthias Bernt¹, Daniel Merkle, Martin Middendorf

¹Parallel Computing and Complex Systems Group, Department of Computer Science, University of Leipzig Augustusplatz 10/11, 04109 Leipzig, Germany.

Bioinformatics (Oxford, England)

|January 24, 2007

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

ProQSAR: A modular and reproducible framework for small-data QSAR modeling with fit-and-use models.

Journal of cheminformatics·2026

Same author

Finding Pathways in Reaction Networks Guided by Energy Barriers Using Integer Linear Programing.

Molecular informatics·2026

Same author

Correction: High-content screening (HCS) workflows for FAIR image data management with OMERO.

Scientific reports·2026

Same author

A Sensitivity Analysis Methodology for Rule-Based Stochastic Chemical Systems.

Journal of chemical information and modeling·2026

Same author

Computational approaches in chemical space exploration for carbon fixation pathways.

NPJ systems biology and applications·2026

Same author

SynKit: A Graph-Based Python Framework for Rule-Based Reaction Modeling and Analysis.

Journal of chemical information and modeling·2025

Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026

Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026

Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026

Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026

Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026

Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026

See all related articles

A new heuristic algorithm, amGRP, considers all possible medians for the reversal median problem (RMP) to reconstruct phylogenetic trees. This approach improves solution quality and computation time compared to existing methods for multiple genome rearrangement problems.

Area of Science:

Computational Biology
Bioinformatics
Evolutionary Biology

Background:

Phylogenetic tree reconstruction often involves solving the reversal median problem (RMP).
Existing algorithms select only one median for each RMP instance, potentially missing optimal solutions.
A diverse set of medians can significantly impact phylogenetic tree accuracy.

Purpose of the Study:

To introduce a novel heuristic algorithm, amGRP, for the multiple genome rearrangement problem (MGRP).
To address the limitation of existing methods by considering all possible medians in RMP instances.
To evaluate the performance of amGRP against standard algorithms using artificial and real-world datasets.

Main Methods:

amGRP employs a branch-and-bound strategy to explore a subset of medians for each RMP instance.

Related Experiment Videos

Investigated various heuristics for selecting subsets of medians.

Compared amGRP with GRAPPA-DCM and MGR on generated gene orders and mitochondrial DNA data.

Main Results:

amGRP demonstrated superior performance in solution quality and computation time.
Analysis revealed significant variations in RMP medians, impacting phylogenetic reconstruction.
Phylogenetic trees generated by amGRP showed improved accuracy on test datasets.

Conclusions:

amGRP offers a more effective approach to phylogenetic tree reconstruction by leveraging the full spectrum of RMP medians.
The algorithm's ability to consider multiple medians leads to better phylogenetic inference.
The findings suggest that exploring diverse medians is crucial for accurate evolutionary analyses.