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

Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

523
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...
523
Microbial Classification System01:24

Microbial Classification System

799
Classification is the process of organizing organisms into hierarchically inclusive groups based on their phenotypic similarities or evolutionary relationships. A species comprises one or more strains, and closely related species are grouped into genera. Genera are further classified into families, families into orders, orders into classes, and so forth, up to the domain level, which is the broadest taxonomic rank derived from a combination of phenotypic and genotypic data.The nomenclature of...
799
Applications of Molecular Taxonomy01:20

Applications of Molecular Taxonomy

450
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...
450
Methods of Classification and Identification01:28

Methods of Classification and Identification

885
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
885

You might also read

Related Articles

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

Sort by
Same author

Reference-free discovery with barcoded single-cell sequencing.

Nature biotechnology·2026
Same author

FunctionaL Assigning Sequence Homing (FLASH) maps phenotype to sequence with deep and machine learning.

bioRxiv : the preprint server for biology·2026
Same author

Fast and accurate multiple-protein-sequence alignment at scale with FAMSA2.

Nature biotechnology·2026
Same author

A Reference-Free Algorithm Discovers Regulation in the Plant Transcriptome.

Plant direct·2026
Same author

An nf-core framework for the systematic comparison of alternative modeling tools: the multiple sequence alignment case study.

NAR genomics and bioinformatics·2025
Same author

Ultrafast and accurate sequence alignment and clustering of viral genomes.

Nature methods·2025

Related Experiment Video

Updated: Jan 3, 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

31.0K

Environmental metagenome classification for constructing a microbiome fingerprint.

Jolanta Kawulok1, Michal Kawulok2, Sebastian Deorowicz2

  • 1Institute of Informatics, Silesian University of Technology, Gliwice, Poland. jolanta.kawulok@polsl.pl.

Biology Direct
|November 15, 2019
PubMed
Summary

This study introduces a new method for predicting the geographical origin of metagenomic samples by analyzing DNA read similarity. This approach bypasses the need for large gene databases, offering a more efficient environmental classification.

Keywords:
CAMDA challengeEnvironmental classificationK-mersMetaSUBMetagenomeSequence classificationUrban microbiome

More Related Videos

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
05:45

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Published on: January 7, 2019

12.0K
Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere
09:55

Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere

Published on: May 2, 2018

28.0K

Related Experiment Videos

Last Updated: Jan 3, 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

31.0K
Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
05:45

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Published on: January 7, 2019

12.0K
Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere
09:55

Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere

Published on: May 2, 2018

28.0K

Area of Science:

  • Genomics
  • Bioinformatics
  • Environmental Science

Background:

  • Metagenome analysis involves studying DNA fragments from microbial communities in environments.
  • Predicting geographical origin of metagenomic samples is a key challenge in environmental classification.

Purpose of the Study:

  • To develop and evaluate a novel method for geographical origin prediction of metagenomic samples.
  • To compare the read-level similarity approach with traditional taxonomic and functional classification methods.

Main Methods:

  • Utilized a read-level similarity comparison against a reference database.
  • Followed the protocol of the MetaSUB Forensics Challenge for experimental validation.

Main Results:

  • The read-level similarity method demonstrated competitive performance against taxonomic classification.
  • The approach successfully predicted geographical origins of metagenomic samples.

Conclusions:

  • Environmental classification of metagenomic data is achievable without extensive annotated gene databases.
  • Read-level similarity offers an efficient alternative for metagenomic sample classification.