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

Modern Molecular Taxonomy

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...

Applications of Molecular Taxonomy

Applications of Molecular Taxonomy

Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...

Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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

STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same author

ZFP148 is a transcriptional repressor of cytolytic effector CD8<sup>+</sup> T cell differentiation.

Nature immunology·2026

Same author

A multi-agent platform for assessment and improvement of bioinformatics software documentation.

bioRxiv : the preprint server for biology·2026

Same author

Understanding Clinical Reasoning Variability in Medical Large Language Models: A Mechanistic Interpretability Study.

medRxiv : the preprint server for health sciences·2026

Same author

MetaMDA: explainable prediction of microbe-drug association utilizing random walks on a microbe-metabolite-drug heterogeneous network.

Bioinformatics (Oxford, England)·2025

Same author

Inferring Gene Regulatory Networks From Single-Cell RNA Sequencing Data by Dual-Role Graph Contrastive Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025

Same journal

STED: flexible cross-modal topic modeling infers cell-type-specific regulatory landscapes from bulk epigenomics.

Briefings in bioinformatics·2026

Same journal

A knowledge-guided deep learning framework for quantitative nucleic acid testing.

Briefings in bioinformatics·2026

Same journal

Optimal transport for label transfer in single-cell multi-omics integration.

Briefings in bioinformatics·2026

Same journal

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026

Same journal

Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Briefings in bioinformatics·2026

Same journal

Transformers for single-cell RNA sequencing: a survey.

Briefings in bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 28, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

Network analyses in microbiome based on high-throughput multi-omics data.

Zhaoqian Liu¹, Anjun Ma¹, Ewy Mathé¹

¹Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH 43210, USA.

Briefings in Bioinformatics

|February 13, 2020

Summary

This summary is machine-generated.

Network analysis offers a powerful approach to understanding complex microbial communities and their interactions. This study reviews network modeling methods for microbiome research, aiding disease and ecological change insights.

Keywords:

co-occurrence integrated analysis microbiome multi-omics network analysis

More Related Videos

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

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing

Published on: March 19, 2018

Related Experiment Videos

Last Updated: Dec 28, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

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

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing

Published on: March 19, 2018

Area of Science:

Microbiome research
Systems biology
Bioinformatics

Background:

Microbial communities are integral to hosts and environments, with alterations linked to diseases and ecological shifts.
High-throughput Omics technologies provide data on microbiome structure and function, but analysis remains challenging due to data complexity and heterogeneity.
Understanding these intricate microbial systems is crucial for biological and biomedical advancements.

Purpose of the Study:

To systematically illustrate network theories applied in biological and biomedical research.
To review existing network modeling methods for microbial studies across multiple omics layers (metagenomics, metabolomics, multi-omics).
To discuss current limitations and propose future directions for microbiome network analysis.

Main Methods:

Systematic review of network theories and their applications in microbiome research.
Comprehensive survey of network modeling approaches from metagenomics to multi-omics data.
Analysis of challenges and opportunities in microbial community data interpretation.

Main Results:

Network analyses are presented as an efficient strategy for elucidating complex microbial communities.
A review of various network modeling techniques applicable to microbiome data is provided.
The study highlights the utility of network approaches in integrating multi-omics data for a holistic understanding.

Conclusions:

Network analysis is a key tool for deciphering microbial community structures and functions.
Further development of network modeling methods is essential for advancing microbiome research.
Addressing current limitations will enhance our ability to understand host-microbe-environment interactions and their implications.