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...

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...

Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes

Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes

The present-day mitochondrial and chloroplast genomes have retained some of the characteristics of their ancestral prokaryotes and also have acquired new attributes during their evolution within eukaryotic cells. Like prokaryotic genomes, mitochondrial and chloroplast genomes neither bind with histone-like proteins nor show complex packaging into chromosome-like structures, as observed in eukaryotes. Unlike mitotic cell divisions observed in eukaryotic cells, mitochondria and chloroplasts...

You might also read

Related Articles

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

Sort by

Same author

A deep learning model for automated identification of age-related macular degeneration atrophy.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie·2026

Same author

One Size Might Not Fit All: A Tailored Approach to Psychological Intergroup Interventions.

Personality & social psychology bulletin·2026

Same author

Broken Promises: Betrayal and Support for Violence in Intergroup Relations.

Personality & social psychology bulletin·2026

Same author

A novel momentum-based intervention sustains real-life participation in a social movement.

Scientific reports·2026

Same author

The disappointing (not hateful) divide: uncovering the negative emotions at the heart of affective polarization.

Cognition & emotion·2026

Same author

A global Youth Peacebuilding Beliefs Scale.

Communications psychology·2026

Search research articles

Related Experiment Video

Updated: Dec 19, 2025

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

Published on: January 10, 2025

Joint analysis of multiple metagenomic samples.

Yael Baran¹, Eran Halperin

¹School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.

Plos Computational Biology

|February 24, 2012

Summary

Analyzing multiple metagenomic samples together improves microbial community characterization. This approach enhances association studies and microbial binning by leveraging shared components and integrated information across samples.

Area of Science:

Microbiology
Bioinformatics
Computational Biology

Background:

Metagenomic sequencing generates DNA data from microbial communities.
Analyzing individual samples separately limits comprehensive characterization.
Existing methods for metagenomic analysis have limitations.

Purpose of the Study:

To develop a method for combined analysis of multiple metagenomic samples.
To improve characterization of microbial communities within samples.
To present two applications of multi-sample analysis.

Main Methods:

Unsupervised probabilistic mixture model to infer shared components across samples.
Novel framework for association studies correcting for stratification.
Multi-sample read clustering (binning) algorithm.

More Related Videos

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

Metagenomic Analysis of Silage

Metagenomic Analysis of Silage

Published on: January 13, 2017

Related Experiment Videos

Last Updated: Dec 19, 2025

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

Published on: January 10, 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

Metagenomic Analysis of Silage

Metagenomic Analysis of Silage

Published on: January 13, 2017

Main Results:

Combined analysis enhances characterization of individual metagenomic samples.
The probabilistic model corrects for false discoveries in association studies.
Multi-sample binning yields more precise results compared to single-sample methods.
Performance generally increases with more samples, given sufficient per-sample coverage.

Conclusions:

Simultaneous analysis of multiple metagenomic samples offers significant advantages.
The proposed methods improve accuracy in association studies and microbial binning.
Integrating information across samples is key to robust metagenomic analysis.