Search research articles

Related Concept Videos

Microbial Growth Measurement: Direct Methods

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 Methods

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...

You might also read

Related Articles

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

Sort by

Same author

Ill Fate of Rectal Mucinous Adenocarcinoma: A Defect in Immunosurveillance or a Mucin Coating Effect?-The IMMUNOREACT 20 Study.

Cancers·2026

Same author

IMMUNOREACT 4: Peritumoral Microenvironment Associated with Anastomotic Leaks After Surgery for Rectal Cancer.

Cancers·2026

Same author

MOV&RSim: computational modelling of cancer-specific variants and sequencing reads characteristics for realistic tumoral sample simulation.

BMC bioinformatics·2025

Same author

The BRAINTEASER Datasets: Clinical, Wearable and Environmental Data for ALS & MS Progression Modeling.

Scientific data·2025

Same author

Differential cellular communication inference framework for large-scale single-cell RNA-sequencing data.

NAR genomics and bioinformatics·2025

Same author

Advances and challenges in cell-cell communication inference: a comprehensive review of tools, resources, and future directions.

Briefings in bioinformatics·2025

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

Search research articles

Related Experiment Video

Updated: Aug 29, 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

Investigating differential abundance methods in microbiome data: A benchmark study.

Marco Cappellato¹, Giacomo Baruzzo¹, Barbara Di Camillo^1,2

¹Department of Information Engineering, University of Padova, Padova, Italy.

Plos Computational Biology

|September 8, 2022

Summary

This summary is machine-generated.

This study evaluated microbial differential abundance analysis methods using simulated data. Most methods control errors well with large sample sizes, but recall varies with dataset and sample size.

More Related Videos

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

A Method to Assess Bacteriocin Effects on the Gut Microbiota of Mice

A Method to Assess Bacteriocin Effects on the Gut Microbiota of Mice

Published on: July 25, 2017

Related Experiment Videos

Last Updated: Aug 29, 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

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

A Method to Assess Bacteriocin Effects on the Gut Microbiota of Mice

A Method to Assess Bacteriocin Effects on the Gut Microbiota of Mice

Published on: July 25, 2017

Area of Science:

Microbiology
Bioinformatics
Computational Biology

Background:

High-throughput DNA sequencing enables complex microbial system studies.
Differential abundance analysis identifies taxa differences between sample groups.
Microbiome data presents challenges: sparsity, variable sequencing depth, and compositionality.

Purpose of the Study:

To benchmark differential abundance analysis methods using simulated microbiome data.
To evaluate method performance across various scenarios and covariates.
To assess the reliability of simulated data and evaluation metrics.

Main Methods:

Utilized metaSPARSim, a microbial sequencing count data simulator, to generate data with differential abundance.
Performed a comprehensive comparison of established and recent bioinformatic methods.
Investigated method performance across scenarios varying in sample size, sequencing depth, and feature characteristics.

Main Results:

Most methods demonstrated good control of Type I error and False Discovery Rate at high sample sizes.
Recall performance was found to be dependent on the specific dataset and sample size.
The study provides a reliable benchmark for evaluating differential abundance analysis tools.

Conclusions:

Differential abundance analysis methods are generally reliable, especially with sufficient sample sizes.
Method selection may require consideration of dataset-specific factors and sample size for optimal recall.
The simulated benchmark aids in understanding method performance and improving microbiome data analysis.