Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A Median Solver and Phylogenetic Inference Based on Double-Cut-and-Join Sorting.

Ruofan Xia1,2, Yu Lin3, Jun Zhou2

  • 11 School of Computer Science and Technology, Tianjin University , Tianjin, China .

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|October 17, 2017
PubMed
Summary

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

Light-driven intracellular and extracellular polymer dynamics regulate colony morphology and buoyancy in Microcystis.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

From dirty work to safety performance: A mixed-methods study on chemical workers' safety compliance and participation in China.

Work (Reading, Mass.)·2026
Same author

Clinically Suspected Whipple Disease Presenting with Acute Kidney Injury as the Initial Manifestation and Complicated by Severe Pneumonia Requiring Veno-Venous Extracorporeal Membrane Oxygenation.

Infection and drug resistance·2026
Same author

An Interpretable Deep Learning Framework Leveraging RNA Foundation Model and Capsule Networks for Accurate Prediction of RNA 2'-O-Methylation Sites.

Journal of chemical information and modeling·2026
Same author

Lexical Inference in L2 Chinese: Does Contextual Information Always Work?

Journal of psycholinguistic research·2026
Same author

Single-cell Raman spectroscopy-machine learning combination: Rapid and accurate strain-level identification of Bifidobacterium animalis subsp. lactis J12.

Food microbiology·2026

This study introduces a novel median solver for gene order data, improving phylogenetic inference. The new method enhances scalability for analyzing large and distant genomes, outperforming existing approaches.

Area of Science:

  • Genomics
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Genome rearrangement is a key evolutionary mechanism driving genomic change.
  • Phylogenetic analysis using gene order is crucial for understanding evolutionary relationships, especially with abundant genomic data.
  • Existing methods for phylogenetic inference, particularly those addressing the small parsimony problem (SPP) and big parsimony problem (BPP) via the median problem, struggle with scalability for large and divergent genomes.

Purpose of the Study:

  • To develop a more scalable and efficient median solver for gene order data.
  • To construct a novel phylogenetic inference method capable of solving both SPP and BPP.
  • To improve the performance of phylogenetic analysis for large and evolutionarily distant genomes.

Main Methods:

Keywords:
big phylogeny problemmedian problemphylogenetic inferencesimulated annealingsmall phylogeny problem

Related Experiment Videos

  • A new median solver was developed by integrating double-cut-and-join sorting with the simulated annealing algorithm.
  • This novel median solver was utilized to build a new phylogenetic inference tool.
  • The method was tested on simulated datasets to evaluate its performance.

Main Results:

  • The proposed median solver demonstrated excellent performance on simulated datasets.
  • The developed phylogenetic inference tool showed superior performance compared to existing methods.
  • The new approach addresses the scalability limitations of current phylogenetic inference techniques.

Conclusions:

  • The novel median solver and associated phylogenetic inference method offer a significant advancement in analyzing genome rearrangement data.
  • This approach provides a more scalable solution for phylogenetic analysis, particularly for large and distant genomes.
  • The findings suggest a promising new direction for evolutionary genomics research.