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

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...

You might also read

Related Articles

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

Sort by
Same author

A U1-U3 snRNA-snoRNA interaction couples SF3B1 mutation to chromatin-state rewiring and genome instability.

bioRxiv : the preprint server for biology·2026
Same author

Mutant SRSF2-associated impaired erythropoiesis is defined by increased mTORC1 signaling due to FYN missplicing.

Leukemia·2026
Same author

An Isoform-Specific RUNX1C-BTG2 Axis Governs AML Quiescence and Chemoresistance.

Blood cancer discovery·2025
Same author

Chorionic Gonadotropin Beta 7 is a marker of immune evasion in cancer.

bioRxiv : the preprint server for biology·2025
Same author

Muscleblind-like proteins are novel modulators of the tumor-immune microenvironment.

PloS one·2025
Same author

Mis-splicing-derived neoantigens and cognate TCRs in splicing factor mutant leukemias.

Cell·2025

Related Experiment Video

Updated: Jun 29, 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

Tools for simulating evolution of aligned genomic regions with integrated parameter estimation.

Avinash Varadarajan1, Robert K Bradley, Ian H Holmes

  • 1Biophysics Graduate Group, University of California-Berkeley, CA 94720-3200, USA.

Genome Biology
|October 9, 2008
PubMed
Summary
This summary is machine-generated.

Three new open-source genome simulation tools offer enhanced extensibility and parameter measurement capabilities. These programs outperform existing simulators, aiding in the benchmarking of genome evolution analysis.

More Related Videos

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Related Experiment Videos

Last Updated: Jun 29, 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

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Genome evolution simulations are crucial for benchmarking bioinformatics tools.
  • Existing simulators often lack extensibility and direct parameter measurement from data.
  • There is a need for advanced simulation software to address these limitations.

Purpose of the Study:

  • To introduce three novel open-source simulation programs: GSIMULATOR, SIMGRAM, and SIMGENOME.
  • To provide tools with enhanced extensibility and direct parameter measurement capabilities.
  • To facilitate the reconstruction of ancestral sequences in genomic simulations.

Main Methods:

  • Development of GSIMULATOR for neutral DNA evolution.
  • Development of SIMGRAM for generic structured feature evolution.
  • Development of SIMGENOME for syntenic genome block evolution.
  • Implementation of algorithms for parameter measurement and ancestral sequence reconstruction.

Main Results:

  • All three developed programs (GSIMULATOR, SIMGRAM, SIMGENOME) demonstrate superior performance compared to the leading neutral DNA simulator, DAWG, in benchmark tests.
  • The new tools offer improved extensibility and direct parameter measurement from simulation data.
  • Successful reconstruction of ancestral sequences is supported by the new algorithms.

Conclusions:

  • The new open-source simulation tools address key limitations in current genome evolution simulators.
  • These programs provide valuable, high-performance solutions for benchmarking and analyzing genome evolution.
  • The availability of these tools at http://biowiki.org/SimulationTools promotes wider adoption in the research community.