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,...

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...

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...

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...

You might also read

Related Articles

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

Sort by

Same author

Tree House Explorer: A Novel Genome Browser for Phylogenomics.

Molecular biology and evolution·2022

Same author

Fast algorithms for computing phylogenetic divergence time.

BMC bioinformatics·2017

Same author

Heterogeneous Compression of Large Collections of Evolutionary Trees.

IEEE/ACM transactions on computational biology and bioinformatics·2015

Same author

Geofold: topology-based protein unfolding pathways capture the effects of engineered disulfides on kinetic stability.

Proteins·2011

Same author

An efficient and extensible approach for compressing phylogenetic trees.

BMC bioinformatics·2011

Same author

A new support measure to quantify the impact of local optima in phylogenetic analyses.

Evolutionary bioinformatics online·2011

Search research articles

Related Experiment Video

Updated: Jun 16, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

MrsRF: an efficient MapReduce algorithm for analyzing large collections of evolutionary trees.

Suzanne J Matthews¹, Tiffani L Williams

¹Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA. sjm@cse.tamu.edu

BMC Bioinformatics

|February 4, 2010

Summary

The MapReduce framework, through the MrsRF algorithm, efficiently generates Robinson-Foulds distance matrices for large phylogenetic datasets. This approach offers significant speedups on multi-core systems, aiding in the visualization and clustering of evolutionary trees.

More Related Videos

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

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

Published on: October 16, 2018

Related Experiment Videos

Last Updated: Jun 16, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

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

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

Published on: October 16, 2018

Area of Science:

Computational Biology
Bioinformatics
Evolutionary Biology

Background:

MapReduce is a parallel framework for large-scale applications.
Phylogenetic applications can benefit from parallel processing frameworks.
Generating Robinson-Foulds distance matrices is crucial for analyzing large evolutionary tree sets.

Purpose of the Study:

Evaluate the MapReduce framework for phylogenetic applications.
Introduce MrsRF, a MapReduce-based algorithm for generating Robinson-Foulds distance matrices.
Assess the scalability and performance of MrsRF.

Main Methods:

Implemented the MrsRF algorithm using the MapReduce paradigm.
Tested MrsRF on large biological tree datasets (20,000 and 33,306 trees).
Analyzed performance across different multi-core cluster configurations.

Main Results:

MrsRF achieved a speedup of over 18 on 32 cores for generating Robinson-Foulds distance matrices.
Scalability was demonstrated on large datasets.
Optimal speedup depends on specific multi-core cluster configurations.

Conclusions:

MapReduce is a viable and promising paradigm for multi-core phylogenetic applications.
Testing various multi-core configurations is essential for optimizing performance.
Robinson-Foulds matrices are critical for summarizing large collections of phylogenetic trees.