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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...
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...
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...
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,...
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.

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Related Experiment Video

Updated: Jun 12, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

GIGA: a simple, efficient algorithm for gene tree inference in the genomic age.

Paul D Thomas1

  • 1Evolutionary Systems Biology Group, SRI International, Menlo Park, CA, USA. pdthomas@usc.edu

BMC Bioinformatics
|June 11, 2010
PubMed
Summary
This summary is machine-generated.

A new algorithm, GIGA, efficiently infers gene phylogenies using genomic data and species trees. This method improves accuracy and enables large-scale ortholog identification, advancing evolutionary studies.

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06:40

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

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Related Experiment Videos

Last Updated: Jun 12, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

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G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome
06:40

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Published on: March 22, 2018

Area of Science:

  • Genomics
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Phylogenetic relationships are crucial for understanding gene function across species.
  • Current gene tree reconstruction algorithms face accuracy limitations due to algorithmic and biological factors.
  • Integrating additional data improves accuracy but incurs high computational costs.

Purpose of the Study:

  • To develop a simple, fast algorithm for inferring gene phylogenies.
  • To leverage genomic-scale data, including species trees and complete gene complements.
  • To enhance the accuracy and efficiency of gene tree reconstruction.

Main Methods:

  • The GIGA algorithm constructs gene trees agglomeratively from a distance matrix.
  • It incorporates genomic information, such as species trees and gene complements.
  • GIGA reinterprets the tree at each step based on evolutionary events like duplication and horizontal gene transfer.

Main Results:

  • GIGA efficiently reconstructs large gene families and determines orthologs at scale.
  • The algorithm demonstrates robustness with increasing gene sequence data.
  • GIGA performs well even with simple distance metrics and without distance averaging.

Conclusions:

  • GIGA offers an efficient solution for large-scale phylogenetic reconstruction and ortholog identification.
  • The method facilitates the creation of stable identifiers for genes and their ancestors.
  • GIGA's emphasis on minimizing gene duplication/deletion events offers a distinct algorithmic advantage.