Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Evolutionary Relationships through Genome Comparisons

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...

Convergent Evolution

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.

Gene Evolution - Fast or Slow?

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?

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...

Evolution of Microbial Genome

Evolution of Microbial Genome

Microbial genome evolution is a highly dynamic process shaped by continual gene gain and loss across species and strains. This genomic flexibility allows microorganisms to adapt rapidly to environmental pressures and interactions with other organisms. Central to understanding this diversity is the distinction between the core and pan genomes.The core genome comprises the genes shared by all sampled strains of a species, representing essential functions needed for fundamental cellular processes.

What is Evolutionary History?

What is Evolutionary History?

Scientists record evolutionary history by analyzing fossil, morphological, and genetic data. The fossil record documents the history of life on Earth and provides evidence for evolution. However, both fossil and living organisms offer evidence that outlines Earth’s evolutionary history.

You might also read

Related Articles

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

Sort by

Same author

Enhancing tumor T cell antigen prediction by integrating deep protein representations.

BMC bioinformatics·2026

Same author

seq2ribo: structure-aware integration of machine learning and simulation to predict ribosome location profiles from RNA sequences.

Bioinformatics (Oxford, England)·2026

Same author

Optimizing sparse and skew hashing: faster k-mer dictionaries.

Bioinformatics (Oxford, England)·2026

Same author

Kaminari: a frugal colored index for approximate <i>k</i>-mer queries.

Bioinformatics advances·2026

Same author

Highly Constrained Kinetic Models for Single-Cell Gene Expression Analysis.

bioRxiv : the preprint server for biology·2026

Same author

Data-driven AI system for learning how to run transcript assemblers.

Genome biology·2026

Search research articles

Related Experiment Video

Updated: May 18, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Parsimonious reconstruction of network evolution.

Rob Patro¹, Emre Sefer, Justin Malin

¹Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA. rob@cs.umd.edu.

Algorithms for Molecular Biology : AMB

|September 21, 2012

Summary

This study presents an efficient and accurate parsimony-based method for reconstructing ancestral biological networks. The approach reconstructs evolutionary histories by minimizing gene interaction changes, improving ancestral network inference.

Related Experiment Videos

Last Updated: May 18, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

Computational Biology
Evolutionary Biology
Systems Biology

Background:

Understanding biological network evolution reveals modularity and adaptation to environmental changes.
Reconstructing ancestral networks aids in analyzing topological changes and evolutionary mechanisms.
Ancestral networks are valuable for solving computational biology problems like network alignment.

Purpose of the Study:

To develop a computational framework for reconstructing ancestral biological networks.
To infer evolutionary histories of biological networks efficiently and accurately.

Main Methods:

Introduced a combinatorial framework for encoding network histories.
Developed a fast procedure using gene duplication histories to find network histories minimizing interaction gain/loss.
Utilized a parsimony approach for ancestral network reconstruction.

Main Results:

The method accurately reconstructs common ancestral networks from simulated and real biological data.
The approach achieves near-minimum interaction gain or loss events.
It does not require prior knowledge of the relative ordering of unrelated duplication events.

Conclusions:

The parsimony-based method is efficient and accurate for ancestral network reconstruction.
Considering a broader set of ancestral interactions improves inference accuracy.
The developed software package is available for use.