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 Concept Videos

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...
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 Duplication and Divergence02:37

Gene Duplication and Divergence

The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are characterized.
Convergent Evolution01:54

Convergent Evolution

Evolution shapes the features of organisms over time, ensuring that they are suited for the environments in which they live. Sometimes, selection pressure leads to the rise of similar but unrelated adaptations in organisms with no recent common ancestors, a process known as convergent evolution.The structures that arise from convergent evolution are called analogous structures. They are similar in function even if they are dissimilar in structure. Further, structures can be analogous while also...
Gene Conversion02:08

Gene Conversion

Other than maintaining genome stability via DNA repair, homologous recombination plays an important role in diversifying the genome. In fact, the recombination of sequences forms the molecular basis of genomic evolution. Random and non-random permutations of genomic sequences create a library of new amalgamated sequences. These newly formed genomes can determine the fitness and survival of cells. In bacteria, homologous and non-homologous types of recombination lead to the evolution of new...

You might also read

Related Articles

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

Sort by
Same author

Histopathology-inferred spatial transcriptomics characterizes the tumor microenvironment in 1,500 head and neck tumors and predicts clinical outcomes.

bioRxiv : the preprint server for biology·2026
Same author

Trials for Rare Cancers Are More Successful than those for Common Cancers.

ESMO rare cancers·2026
Same author

Liquid surrogates of spatial tumor ecosystems.

Cell research·2026
Same author

Longitudinal validation of ENLIGHT, an AI predictor of immunotherapy response and resistance, in pan-cancer cohorts.

NPJ precision oncology·2026
Same author

Author Correction: The ubiquitin ligase RNF5 determines acute myeloid leukemia growth and susceptibility to histone deacetylase inhibitors.

Nature communications·2026
Same author

SYNTHESIS-Breast: A prospective early-phase trial of a genetic-interaction- focused computational algorithm in advanced metastatic breast cancer.

Research square·2026

Related Experiment Video

Updated: Jun 18, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Reconstructing ancestral gene content by coevolution.

Tamir Tuller1, Hadas Birin, Uri Gophna

  • 1School of Computer Sciences, Tel Aviv University, Ramat Aviv, Israel. tamirtul@post.tau.ac.il

Genome Research
|December 2, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces the ancestral coevolver (ACE) method, significantly improving ancestral genome reconstruction accuracy by using coevolutionary data. ACE reduces errors compared to maximum likelihood (ML) or maximum parsimony (MP) methods, revealing a more complete picture of the last universal common ancestor (LUCA).

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

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli
15:00

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Published on: August 18, 2023

Related Experiment Videos

Last Updated: Jun 18, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli
15:00

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Published on: August 18, 2023

Area of Science:

  • Molecular Evolution
  • Bioinformatics
  • Genomics

Background:

  • Inferring ancestral genome gene content is crucial but challenging.
  • Existing methods like maximum likelihood (ML) and maximum parsimony (MP) have high error rates.
  • Errors are difficult to reduce with more data or taxa.

Purpose of the Study:

  • To introduce a novel method, the ancestral coevolver (ACE), for more accurate ancestral genome reconstruction.
  • To leverage coevolutionary information to refine gene content predictions.
  • To improve upon the limitations of current ML and MP approaches.

Main Methods:

  • Developed the ancestral coevolver (ACE) method.
  • Utilized coevolutionary relationships between protein families to constrain ancestral genome reconstruction.
  • Performed simulation experiments on artificial and real biological data.
  • Applied ACE to a large dataset of 95 organisms and 4873 protein families.

Main Results:

  • ACE significantly decreased error rates compared to ML and MP methods.
  • Reconstructed ancestral genomes using ACE showed substantial differences (over 10%) in gene content compared to ML/MP.
  • ACE reconstructions demonstrated higher concordance with coevolutionary information.
  • ACE identified numerous additional proteins for the last universal common ancestor (LUCA), including ribosomal proteins and ATP synthase components.

Conclusions:

  • The ancestral coevolver (ACE) method enhances ancestral genome reconstruction accuracy.
  • The last universal common ancestor (LUCA) likely possessed a bacterial-like genome with a size comparable to extant organisms.
  • Coevolutionary data is vital for resolving ambiguities in ancestral genome inference.