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

Applications of Molecular Taxonomy01:20

Applications of Molecular Taxonomy

326
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...
326
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

402
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...
402

You might also read

Related Articles

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

Sort by
Same author

Aryl hydrocarbon Receptor Nuclear Translocator 2: A Forgotten Per-ARNT-Sim Transcription Factor.

Toxicological sciences : an official journal of the Society of Toxicology·2026
Same author

Raman spectral unmixing for quantitative analysis of multicomponent amino acid mixtures.

Analytical and bioanalytical chemistry·2026
Same author

Transcriptional and epigenetic regulation of autophagy: mechanisms, disease relevance and therapeutic opportunities.

Signal transduction and targeted therapy·2026
Same author

Exploring CHO cell stability during prolonged passaging via eXplainable AI driven flux balance analysis.

NPJ systems biology and applications·2026
Same author

Pesco-Vegetarian Food Components Promote Colonocyte Ferroptosis in Preclinical Mouse Models and a Randomized Crossover Trial in Healthy Human Adults.

The Journal of nutrition·2025
Same author

Imaging Findings of Biliary Adenofibroma of the Liver: A Case Report.

Journal of the Korean Society of Radiology·2025

Related Experiment Video

Updated: Nov 28, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
11:22

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

Published on: October 15, 2019

30.7K

Emerging computational tools and models for studying gut microbiota composition and function.

Seo-Young Park1, Arinzechukwu Ufondu2, Kyongbum Lee1

  • 1Department of Chemical and Biological Engineering, Tufts University, Medford, MA, USA.

Current Opinion in Biotechnology
|November 28, 2020
PubMed
Summary
This summary is machine-generated.

Analyzing time-series microbiome and metabolome data is crucial for understanding gut health. This review covers new computational tools and methods for modeling these complex biological datasets.

More Related Videos

Analysis of Interactions between Endobiotics and Human Gut Microbiota Using In Vitro Bath Fermentation Systems
06:58

Analysis of Interactions between Endobiotics and Human Gut Microbiota Using In Vitro Bath Fermentation Systems

Published on: August 23, 2019

7.3K
Visualization of Gut Microbiota-host Interactions via Fluorescence In Situ Hybridization, Lectin Staining, and Imaging
09:31

Visualization of Gut Microbiota-host Interactions via Fluorescence In Situ Hybridization, Lectin Staining, and Imaging

Published on: July 9, 2021

9.1K

Related Experiment Videos

Last Updated: Nov 28, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
11:22

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

Published on: October 15, 2019

30.7K
Analysis of Interactions between Endobiotics and Human Gut Microbiota Using In Vitro Bath Fermentation Systems
06:58

Analysis of Interactions between Endobiotics and Human Gut Microbiota Using In Vitro Bath Fermentation Systems

Published on: August 23, 2019

7.3K
Visualization of Gut Microbiota-host Interactions via Fluorescence In Situ Hybridization, Lectin Staining, and Imaging
09:31

Visualization of Gut Microbiota-host Interactions via Fluorescence In Situ Hybridization, Lectin Staining, and Imaging

Published on: July 9, 2021

9.1K

Area of Science:

  • Microbiology
  • Bioinformatics
  • Systems Biology

Background:

  • The gut microbiota and its metabolites significantly impact human health and disease.
  • High-throughput omics technologies generate vast temporal datasets on microbial communities.
  • Analyzing longitudinal microbiome and metabolome data presents statistical and methodological challenges.

Purpose of the Study:

  • To review recent advancements in computational models and software for analyzing time-series microbiome and metabolome data.
  • To highlight methods for integrating multi-omics data from the gut microbiome.
  • To address challenges in statistical significance, normalization, and validation of time-series data.

Main Methods:

  • Review of recent literature on statistical and computational modeling techniques.
  • Survey of available software tools for time-series microbiome and metabolome analysis.
  • Discussion of data integration strategies for longitudinal multi-omics studies.

Main Results:

  • Emerging models and software tools offer improved capabilities for analyzing temporal microbiome and metabolome variations.
  • New approaches facilitate the integration of diverse omics data types.
  • Progress has been made in addressing sparsity, uneven sampling, and normalization challenges.

Conclusions:

  • Advanced modeling and software tools are essential for deciphering the complex temporal dynamics of the gut microbiome and metabolome.
  • Effective analysis and integration of time-series omics data will accelerate discoveries in gut health and disease.
  • Further development is needed to refine statistical rigor and model validation for these complex datasets.