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...

You might also read

Related Articles

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

Sort by

Same author

From FAIR to CURE: guidelines for computational models of biological systems.

NPJ systems biology and applications·2026

Same author

Collaborative Problem Solving in Mixed Reality: A Study on Visual Graph Analysis.

IEEE transactions on visualization and computer graphics·2026

Same author

<i>MaSpeQC</i>: An easy-to-use Interactive Pipeline to Assess the Performance of LC-MS/MS Instrumentation.

Journal of proteome research·2026

Same author

Systems biology graphical notation: process description language level 1 version 2.1.

Journal of integrative bioinformatics·2026

Same author

UniCor and UniCorP: a novel metric and hierarchical feature selection algorithm for microbial community analysis.

ISME communications·2025

Same author

Genome-scale metabolic modelling in antimicrobial pharmacology: Present and future.

Advanced drug delivery reviews·2025

Same journal

Isolation of Mesenchymal Stem Cell-Derived Extracellular Vesicles.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Modeling Melanoma Immune Surveillance by CAR-T Cells in Human Skin Organoids.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Stepwise Optimization of a Matrigel-Based In Vitro Angiogenesis Assay for Reproducible and Quantifiable 2D-Tube Formation Using HUVECs.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Quantifying Mechanical Properties of Fresh Ovarian Tissue with Optical Brillouin Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

3D Chromatin Architecture During Early Development: New Methods and New Findings.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Metabolic Plasticity in Embryogenesis Throughout the Lens of NAD<sup></sup>.

Methods in molecular biology (Clifton, N.J.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 11, 2026

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

Information Visualization for Biological Data.

Tobias Czauderna¹, Falk Schreiber^2,3

¹Faculty of Information Technology, Monash University, Clayton, VIC, Australia.

Methods in Molecular Biology (Clifton, N.J.)

|November 30, 2016

Summary

This summary is machine-generated.

This chapter introduces data visualization for the life sciences, detailing common bioinformatics techniques and a standard for biological networks. It highlights visualization

Keywords:

Data exploration Force-based layout Graph drawing Heat-maps Systems Biology Graphical Notation Visualization

More Related Videos

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

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

Related Experiment Videos

Last Updated: Mar 11, 2026

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

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:

Bioinformatics and Life Sciences

Background:

Data visualization is crucial for exploring large biological datasets.
Its application spans structural information, high-throughput data, and biochemical networks.

Purpose of the Study:

To provide an introduction to visualization methods in bioinformatics.
To detail two commonly used visualization techniques.
To discuss a graphical standard for biological networks and cellular processes.

Main Methods:

Introduction to general visualization principles.
Detailed explanation of two specific bioinformatics visualization techniques.
Discussion of a graphical standard for biological networks.

Main Results:

Provides foundational knowledge of bioinformatics visualization.
Offers practical insights into selected visualization methods.
Introduces a standard for representing biological networks.

Conclusions:

Effective visualization is key to understanding complex biological data.
Standardized graphical representations enhance communication and analysis of biological networks.