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

Statistically based postprocessing of phylogenetic analysis by clustering.

Cara Stockham1, Li-San Wang, Tandy Warnow

  • 1Texas Institute for Computational and Applied Mathematics, University of Texas, ACES 6.412, Austin TX 78712, USA.

Bioinformatics (Oxford, England)
|August 10, 2002
PubMed
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

A TAD-informed aging-brain xQTL atlas of multi-modal and cell-type-resolved regulatory variation.

medRxiv : the preprint server for health sciences·2026
Same author

Functionally informed annotation influences pathway-specific polygenic risk and disease inference in Alzheimer's disease.

medRxiv : the preprint server for health sciences·2026
Same author

Comprehensive adjudication identifies 111 high-confidence loci for Alzheimer's disease and related dementias.

medRxiv : the preprint server for health sciences·2026
Same author

Min-frame transformation enables more sensitive viral genome alignment.

bioRxiv : the preprint server for biology·2026
Same author

TIPP-SD: A new method for species detection in microbiomes.

PLoS computational biology·2026
Same author

Phylogenetic Placement Using SCAMPP and Batch-SCAMPP.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026
See all related articles

This study introduces characteristic trees, a novel consensus method for phylogenetic analysis. This approach improves information content and tree resolution compared to traditional single-tree consensus methods.

Area of Science:

  • Phylogenetics and evolutionary biology
  • Computational biology and bioinformatics

Background:

  • Phylogenetic analyses generate numerous candidate trees, necessitating consensus methods for interpretation.
  • Existing single-tree consensus methods have limitations in resolving conflicting phylogenetic signals.

Purpose of the Study:

  • To develop an alternative approach for phylogenetic tree consensus using clustering algorithms.
  • To introduce characteristic trees that minimize information loss and improve resolution.

Main Methods:

  • Applied clustering algorithms to sets of candidate phylogenetic trees.
  • Defined bicriterion problems incorporating information loss.
  • Developed characteristic trees as a new consensus method.

Main Results:

Related Experiment Videos

  • The proposed characteristic tree approach significantly improved information content across four biological datasets.
  • This method added minimal complexity while yielding more resolved consensus trees than single-tree methods.
  • Empirical studies demonstrated the effectiveness of characteristic trees in handling large sets of phylogenetic trees.

Conclusions:

  • Characteristic trees offer a superior alternative to traditional consensus methods for phylogenetic analysis.
  • This novel approach enhances the resolution and information content of phylogenetic reconstructions.
  • The study opens avenues for further theoretical and applied research in phylogenetic consensus.