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

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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

p-Cymene-Loaded Sucrose-Functionalized Carbon Dots as Phloem-Targeted Nanocarriers for Precision Control of Kiwifruit Canker.

Journal of food science·2026

Same author

DDX39B drives the m6A modification of LDHA to promote trophoblast proliferation.

Molecular genetics and genomics : MGG·2026

Same author

Nanocarrier-Mediated RNAi: Breaking the dsRNA Delivery Bottleneck in Crop Protection.

Journal of agricultural and food chemistry·2026

Same author

Rsk4 Mediates Brain FGF19 Signaling to Constrain Diet-Induced Obesity in Mice.

Diabetes·2026

Same author

Drought-responsive TaMIP1 controls root density to confer the tradeoff between grain yield and drought resistance in wheat.

Plant physiology·2026

Same author

Nano-Delivery System for the Prevention and Control of the Disease.

Molecules (Basel, Switzerland)·2026

Same journal

Spinal cord injury induces acute microbiome shock and system-wide transcriptomic reprogramming.

iMeta·2026

Same journal

Chondroitin sulfate restores muscle mass via gut-muscle axis remodeling through sugar-bile acid metabolism reprogramming.

iMeta·2026

Same journal

A large-scale single-cell transcriptomic atlas indicates the immune panorama of influenza A infection.

iMeta·2026

Same journal

Accu16S/AccuITS: Accurate and broadly applicable amplicon sequencing for absolute microbiome quantification.

iMeta·2026

Same journal

Chromatin unwinding state for in situ identification and lineage tracing of circulating tumor cells.

iMeta·2026

Same journal

Two stable gut microbiome guilds predict liver tumor class and treatment responses.

iMeta·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 24, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

A guide for comparing microbial co-occurrence networks.

Chi Liu¹, Chaonan Li², Yanqiong Jiang¹

¹Engineering Research Center of Soil Remediation of Fujian Province University, College of Resources and Environment Fujian Agriculture and Forestry University Fuzhou China.

|June 13, 2024

Summary

This summary is machine-generated.

This study introduces a flexible R pipeline for comparing microbial co-occurrence networks, aiding researchers in analyzing microbial community structures across diverse datasets and methods.

More Related Videos

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

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

Published on: November 12, 2012

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

Related Experiment Videos

Last Updated: Jun 24, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

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

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

Published on: November 12, 2012

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

Area of Science:

Microbiology
Bioinformatics
Computational Biology

Background:

Microbial co-occurrence networks are crucial for understanding microbial community structure and function.
Comparing these networks across different sample groups or construction methods is essential but challenging.
Existing tools may lack the flexibility needed for comprehensive network comparison.

Purpose of the Study:

To present a novel, flexible, and expandable R pipeline for comparing microbial co-occurrence networks.
To facilitate efficient comparison of networks derived from various sample groups or construction approaches.
To enhance the analysis of microbial community dynamics through robust network comparison.

Main Methods:

Development of a comparative pipeline utilizing the R microeco and meconetcomp packages.
Implementation of flexible parameters for network comparison.
Designed for high extensibility to accommodate diverse analytical needs.

Main Results:

The pipeline offers a streamlined approach to comparing microbial co-occurrence networks.
Demonstrates high flexibility and expansibility for various comparative analyses.
Enables efficient identification of differences and similarities between networks.

Conclusions:

The developed R pipeline provides a valuable tool for microbial ecologists and bioinformaticians.
Facilitates robust comparative analyses of microbial co-occurrence networks.
Enhances the ability to interpret microbial community structures and interactions.