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

Microbial Growth Measurement: Direct Methods01:23

Microbial Growth Measurement: Direct Methods

1.1K
Direct methods for measuring microbial populations in a culture are essential tools in microbiology, providing quantitative data for various applications. Among these, microscopic counts, plate counts, and serial dilution are widely used techniques, each with unique principles and applications.Microscopic CountsMicroscopic counting involves the use of a Petroff-Hausser chamber, a specialized microscope slide with a grid and defined depth. By observing a liquid culture under a microscope,...
1.1K
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

392
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...
392
Microbial Growth Measurement: Indirect Methods01:27

Microbial Growth Measurement: Indirect Methods

905
Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...
905
Key Techniques in Microbiology01:29

Key Techniques in Microbiology

1.1K
Aseptic techniques prevent contamination, ensure experimental accuracy, and protect researchers and microbial cultures. These techniques are essential in clinical, industrial, and research settings where sterility is required.Maintaining Sterility in Laboratory PracticesScientists maintain sterility by sterilizing tools with heat or chemicals, disinfecting work surfaces, and handling cultures in controlled environments. Working near an open flame or within a laminar flow hood reduces the risk...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Spatial heterogeneity and microbial terroir: balancing dispersal limitation and cultivar as drivers of microbial diversity in viticulture.

ISME communications·2026
Same author

The gut microbiota and sleep in infants: a focus on diurnal rhythmicity patterns.

Gut microbes reports·2026
Same author

Evaluating long-term stool preservation methods for maximizing the recovery of viable human fecal microbiota.

Gut microbes reports·2026
Same author

Implications of Cyclophosphamide, Methotrexate, and 5-Flurouracil Chemotherapy on Hippocampal-Dependent Cognition and Gut-Microbiome.

Frontiers in microbiomes·2026
Same author

HighALPS: ultra-high-throughput marker-gene amplicon library preparation and sequencing on the Illumina NextSeq and NovaSeq Platforms.

mSystems·2026
Same author

Late-in-life treadmill training mitigates gut microbiome imbalances and cardiovascular disease risk in mice.

American journal of physiology. Gastrointestinal and liver physiology·2026
Same journal

Functional Genomic Evidence for Candidate Small Viral RNA-Mediated Epigenetic Interference in SARS-CoV-1 and SARS-CoV-2.

Computational and structural biotechnology journal·2026
Same journal

From Pixels to Patterns: A Multidimensional Framework to Decode Cytoskeletal Organization.

Computational and structural biotechnology journal·2026
Same journal

A Large Concept Model for Mechanistic Simulation of Disease Trajectories: A Hypothesis-Generating Exemplar for Pediatric Acute Lymphoblastic Leukemia.

Computational and structural biotechnology journal·2026
Same journal

Adversarial Sequence Mutations in AlphaFold and ESMFold Reveal Nonphysical Structural Invariance, Confidence Failures, and Concerns for Protein Design.

Computational and structural biotechnology journal·2026
Same journal

High-Throughput Prediction of Protein-Protein Interactions Uncovers Hidden Molecular Networks in Biosynthetic Gene Clusters.

Computational and structural biotechnology journal·2026
Same journal

A Region-Aware Structured Framework Improves Prediction of Gene Expression from DNA Methylation.

Computational and structural biotechnology journal·2026
See all related articles

Related Experiment Video

Updated: Nov 24, 2025

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing
08:05

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

Published on: March 19, 2018

20.3K

Measuring the microbiome: Best practices for developing and benchmarking microbiomics methods.

Nicholas A Bokulich1, Michal Ziemski1, Michael S Robeson2

  • 1Laboratory of Food Systems Biotechnology, Institute of Food, Nutrition, and Health, ETH Zurich, Switzerland.

Computational and Structural Biotechnology Journal
|December 28, 2020
PubMed
Summary
This summary is machine-generated.

This review outlines best practices for computational methods in microbiomics. It emphasizes benchmarking and validating tools for analyzing complex microbial datasets, crucial for understanding host-associated microbial communities.

Keywords:
Amplicon sequencingBenchmarkingBest practicesMarker-gene sequencingMetagenomicsMicrobiomeSoftware development

More Related Videos

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
Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
07:21

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

13.2K

Related Experiment Videos

Last Updated: Nov 24, 2025

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing
08:05

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

Published on: March 19, 2018

20.3K
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
Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
07:21

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

13.2K

Area of Science:

  • Microbiology
  • Computational Biology
  • Bioinformatics

Background:

  • Microbiomes are essential in ecosystems and host health (human, animal, plant).
  • Microbiome complexity necessitates robust computational analysis methods.
  • Marker-gene and metagenome data are key for microbiome studies.

Purpose of the Study:

  • To review best practices for computational methods in microbiomics.
  • To guide the development, optimization, and validation of microbiome analysis software.
  • To highlight unique data characteristics for method design.

Main Methods:

  • Literature review of computational methods for microbiome analysis.
  • Focus on benchmarking and validation strategies.
  • Consideration of unique microbiome data properties.

Main Results:

  • Identified key considerations for computational method development in microbiomics.
  • Provided best practices for analyzing marker-gene and metagenome data.
  • Highlighted the importance of validating methods against microbiome data characteristics.

Conclusions:

  • Effective microbiome research relies on well-benchmarked computational tools.
  • Method development must account for microbiome data complexity.
  • Standardized practices are needed for reliable microbiomics research.