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

Methods to Assess Microbial Communities01:19

Methods to Assess Microbial Communities

Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...
Methods to Assess Microbial Populations01:30

Methods to Assess Microbial Populations

Assessing microbial populations is crucial for understanding microbial roles in health, ecology, and industry. Various complementary techniques—both culture-based and molecular—enable detailed analysis of microbial abundance, diversity, and function.Viable Plate CountThe viable plate count is a traditional culture-based method used to estimate the number of living microbes in a sample. After serial dilution, the sample is spread onto nutrient agar plates. Each viable cell forms a visible...
Microbial Growth Measurement: Direct Methods01:23

Microbial Growth Measurement: Direct Methods

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

Microbial Growth Measurement: Indirect Methods

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...
Modern Molecular Taxonomy01:29

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 Taxonomy01:20

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...

You might also read

Related Articles

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

Sort by
Same author

Genome-Wide Analysis of an Endangered Axolotl Endemic to Mexico Reveals Genomic Variation Associated with Body Condition, Environment, and Infection by a Pathogenic Fungus.

Genome biology and evolution·2026
Same author

The Origin of Life in the Light of Evolution.

ArXiv·2026
Same author

Rescue Inhaler Overuse in Severe Asthma: A Real-World Study of Short-Acting β<sub>2</sub>-Agonist and Short-Acting Muscarinic Antagonist Use.

Biomedicines·2026
Same author

Real-World Effectiveness of Tezepelumab Across T2 and Non-T2 Severe Asthma Phenotypes: A Multicenter Spanish Cohort Study.

Archivos de bronconeumologia·2026
Same author

Adaptive Benefits of Hybridization in Saccharomyces Yeast are Constrained by Genomic Background and Depend on Temperature.

Evolution; international journal of organic evolution·2026
Same author

Beyond Global Models: Mapping the Spatially Contingent Relationship Between Soil Sand Content and Woody Invasion.

Life (Basel, Switzerland)·2026

Related Experiment Video

Updated: May 22, 2026

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

Understanding microbial community diversity metrics derived from metagenomes: performance evaluation using simulated

Germán Bonilla-Rosso1, Luis E Eguiarte, David Romero

  • 1Department of Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de México, México D.F, México.

FEMS Microbiology Ecology
|May 5, 2012
PubMed
Summary

Metagenomic analysis of community diversity reveals that protein-coding genes offer a more accurate representation than SSU-rRNA. Commonly used diversity metrics are biased, highlighting the need for improved methods in ecological studies.

More Related Videos

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

Related Experiment Videos

Last Updated: May 22, 2026

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

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

Area of Science:

  • Ecology
  • Bioinformatics
  • Computational Biology

Background:

  • Metagenomics enables the study of microbial community diversity but requires tailored theoretical and methodological approaches.
  • Evaluating existing macroecological metrics on metagenomic data is crucial for accurate community structure analysis.

Purpose of the Study:

  • To assess the suitability of widely used taxonomic diversity metrics for metagenomic data.
  • To compare the effectiveness of protein-coding genes versus SSU-rRNA genes as ecological proxies in metagenomic studies.
  • To identify biases in current diversity metrics and propose improvements for metagenomic data analysis.

Main Methods:

  • Simulated metagenomic samples were used to evaluate macroecological metrics of taxonomic diversity.
  • Phylogenetically meaningful protein-coding genes were employed as ecological proxies.
  • Abundance matrices derived from protein-coding marker genes and SSU-rRNA genes were compared.
  • Commonly used diversity metrics were analyzed for biases against true community parameters.

Main Results:

  • Abundance matrices from protein-coding marker genes more accurately reflected the original community structure compared to SSU-rRNA.
  • Standard diversity metrics demonstrated significant biases, likely due to undersampling and phylogenetic composition differences.
  • Multidimensional metrics for ranking samples provided a useful qualitative assessment of community structure.

Conclusions:

  • Protein-coding genes are superior to SSU-rRNA for reconstructing community structure in metagenomic analyses.
  • Current diversity metrics require refinement to mitigate biases inherent in metagenomic data.
  • Integrating community structure and phylogenetic diversity metrics can enhance comparative analyses and establish a standardized framework for metagenomic data.