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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

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 Phylogeny01:28

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,...
Gene Evolution - Fast or Slow?02:05

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...
Phylogeny01:23

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...
Phylogenetic Trees03:21

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 Trees03:21

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...

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Related Experiment Video

Updated: Jul 10, 2026

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
10:23

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

Published on: July 11, 2025

Ultrafast and ultralarge distance-based phylogenetics using DIPPER.

Sumit Walia1, Zexing Chen1, Yu-Hsiang Tseng1

  • 1Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA, USA.

Nature Computational Science
|July 8, 2026
PubMed
Summary
This summary is machine-generated.

DIPPER is a new GPU-accelerated tool that reconstructs phylogenetic trees faster and with less memory. It enables efficient analysis of large biological sequence datasets, overcoming limitations of classical methods.

More Related Videos

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Related Experiment Videos

Last Updated: Jul 10, 2026

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
10:23

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

Published on: July 11, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Phylogenetics

Background:

  • Distance-based phylogenetic reconstruction methods offer speed and scalability but face computational challenges with large datasets.
  • Classical de novo algorithms exhibit cubic time and quadratic memory complexity, limiting their application to millions of sequences.

Purpose of the Study:

  • To introduce DIPPER, a novel graphics processing unit (GPU)-based tool for efficient and accurate distance-based phylogenetic reconstruction.
  • To address the limitations of existing methods for analyzing large-scale biological sequence data.

Main Methods:

  • DIPPER utilizes a graphics processing unit (GPU) for accelerated computation.
  • Employs a divide-and-conquer strategy, a placement strategy, and an on-the-fly distance calculator.
  • Achieves O(N log N) runtime and O(N) memory complexity, with a GPU memory footprint independent of the number of taxa (N).

Main Results:

  • DIPPER demonstrates superior speed and memory efficiency compared to existing phylogenetic reconstruction methods.
  • Successfully reconstructs phylogenies for datasets with up to 10 million sequences in under 7 hours on a single GPU.
  • Maintains high accuracy while significantly reducing computational resource requirements.

Conclusions:

  • DIPPER provides a scalable and efficient solution for large-scale phylogenetic reconstruction.
  • Enables researchers to analyze unprecedentedly large sequence datasets, advancing evolutionary biology and related fields.
  • GPU acceleration and optimized algorithms overcome previous computational bottlenecks.