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 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.
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...
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.
Genome Copying Errors02:46

Genome Copying Errors

DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

You might also read

Related Articles

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

Sort by
Same author

Genetic and epidemiological evidence linking respiratory and musculoskeletal diseases: shared risk factors and intervention windows.

Respiratory research·2026
Same author

Genetic and lifestyle modifiers of haemochromatosis-related clinical outcomes in HFE C282Y homozygotes.

JHEP reports : innovation in hepatology·2026
Same author

A Google Maps-Based Method to Create a National Emergency Department Database.

Annals of emergency medicine·2026
Same author

Genetics identifies obesity as a shared risk factor for co-occurring multiple long-term conditions.

Communications medicine·2026
Same author

Liver iron levels are associated with HFE-hemochromatosis genotype, diet, adiposity, and disease in the UK Biobank.

Hepatology communications·2026
Same author

Evolution of Secondary Findings in Acute Cholecystitis: A Temporal Analysis from Point-of-Care Ultrasound to Subsequent Imaging.

The Journal of emergency medicine·2025

Related Experiment Video

Updated: Jun 8, 2026

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

Computing the summed adjacency disruption number between two genomes with duplicate genes.

João Delgado1, Inês Lynce, Vasco Manquinho

  • 1Instituto Superior Técnico, Technical University of Lisbon and INESC-ID Lisboa, Rua Alves Redol 9, Lisbon, Portugal.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|September 30, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces new algorithms for genome rearrangement analysis, specifically addressing duplicate genes. The methods accurately calculate gene order similarity in genomes with duplicated genes.

More Related Videos

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis
10:08

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis

Published on: August 12, 2019

A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe
07:55

A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe

Published on: March 7, 2019

Related Experiment Videos

Last Updated: Jun 8, 2026

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis
10:08

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis

Published on: August 12, 2019

A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe
07:55

A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe

Published on: March 7, 2019

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome sequencing advancements necessitate sophisticated genome rearrangement analysis.
  • Existing methods struggle with efficiency or complexity, especially for genomes with duplicate genes.
  • Duplicate genes present a significant challenge in comparative genomics.

Purpose of the Study:

  • To develop novel algorithms for accurately analyzing genome rearrangements in the presence of duplicate genes.
  • To define and compute similarity measures that account for gene duplication.
  • To address the limitations of current genome rearrangement methodologies.

Main Methods:

  • Development of new algorithms for computing the exact summed adjacency disruption number.
  • Implementation of matching models to handle duplicate genes.
  • Evaluation of gene order preservation using defined similarity measures.

Main Results:

  • The proposed algorithms efficiently compute the exact summed adjacency disruption number for genomes with duplicate genes.
  • The new methods provide a robust way to disambiguate duplicate gene data.
  • Experimental validation on a γ-Proteobacteria dataset demonstrates the approach's effectiveness.

Conclusions:

  • The developed algorithms offer a significant advancement in analyzing genome rearrangements with duplicate genes.
  • This work provides a more accurate and applicable method for comparative genomics.
  • The approach is suitable for complex organisms and large-scale genomic datasets.