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...

Microbial Phylogeny

Microbial Phylogeny

Understanding the evolutionary relationships among microorganisms is fundamental to microbial ecology and taxonomy. Phylogenetic trees are essential tools for inferring these relationships, relying primarily on comparative analyses of molecular sequences such as DNA, RNA, or proteins. In microbial studies, these trees typically depict the evolutionary paths of diverse bacterial and archaeal species by mapping genetic differences accumulated over time.Phylogenetic trees are composed of tips,...

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...

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...

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...

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.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...

You might also read

Related Articles

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

Sort by

Same author

Lower Bounds on the Sample Complexity of Species Tree Estimation when Substitution Rates Vary Across Loci.

Bulletin of mathematical biology·2025

Same author

Errors are robustly tamed in cumulative knowledge processes.

Proceedings of the National Academy of Sciences of the United States of America·2025

Same author

Maximum Likelihood Estimation for Unrooted 3-Leaf Trees: An Analytic Solution for the CFN Model.

Bulletin of mathematical biology·2024

Same author

Long ties accelerate noisy threshold-based contagions.

Nature human behaviour·2024

Same author

QR-STAR: A Polynomial-Time Statistically Consistent Method for Rooting Species Trees Under the Coalescent.

Journal of computational biology : a journal of computational molecular cell biology·2023

Same author

Efficient Reconstruction of Stochastic Pedigrees: Some Steps From Theory to Practice.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2022

Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024

Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024

Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024

Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024

Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024

Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 16, 2026

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Incomplete lineage sorting: consistent phylogeny estimation from multiple loci.

Elchanan Mossel¹, Sebastien Roch

¹Department of Statistics, University of California-Berkeley, Berkeley, CA 94720-3860, USA. mossel@stat.berkeley.edu

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|February 13, 2010

Summary

This summary is machine-generated.

We developed a fast algorithm to build species phylogenies from gene trees, even with incomplete lineage sorting. This method accurately reconstructs evolutionary history using multiple gene copies and tolerates minor errors.

More Related Videos

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Related Experiment Videos

Last Updated: Jun 16, 2026

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

Area of Science:

Computational Biology
Phylogenetics
Evolutionary Genetics

Background:

Reconstructing species phylogenies is crucial for understanding evolutionary history.
Incomplete lineage sorting (ILS) complicates phylogenetic reconstruction as gene trees may not match the species tree topology.
Existing methods may be computationally intensive or sensitive to ILS.

Purpose of the Study:

To introduce a computationally efficient algorithm for species phylogeny reconstruction.
To address the challenge of incomplete lineage sorting in phylogenetic analysis.
To provide a statistically consistent method for inferring species trees from gene trees.

Main Methods:

Developed a simple, computationally efficient algorithm.
Utilized multiple gene trees as input data.
Assessed statistical consistency under standard stochastic models.

Main Results:

The algorithm successfully reconstructs phylogenies in the presence of incomplete lineage sorting.
Demonstrated statistical consistency, converging to the correct species tree with sufficient unlinked loci.
Showed tolerance to moderate gene tree estimation errors.

Conclusions:

The proposed algorithm offers an efficient and robust approach to species phylogeny reconstruction.
It provides a statistically sound method for inferring evolutionary relationships despite ILS.
The technique is practical for analyzing genomic data with potential estimation inaccuracies.