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

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples11:23

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

37.6K
Using the cystic fibrosis airway as an example, the manuscript presents a comprehensive workflow comprising a combination of metagenomic and metatranscriptomic approaches to characterize the microbial and viral communities in animal-associated...
37.6K
Nanopore DNA Sequencing for Metagenomic Soil Analysis07:33

Nanopore DNA Sequencing for Metagenomic Soil Analysis

31.6K
Nanopore technology for sequencing biomolecules has wide applications in the life sciences, including identification of pathogens, food safety monitoring, genomic analysis, metagenomic environmental monitoring, and characterization of bacterial antibiotic resistance. In this article, the procedure for metagenomic soil DNA sequencing for species identification using the nanopore sequencing technology is...
31.6K
Rare Event Detection Using Error-corrected DNA and RNA Sequencing10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

12.6K
Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5–2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as...
12.6K
Metagenomic Analysis of Silage08:43

Metagenomic Analysis of Silage

19.0K
Metagenomics was used to investigate the microbiome of silage cattle feed. Analysis was performed by shotgun sequencing. This approach was used to characterize the composition of the microbial community within the cattle...
19.0K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

4.4K
This tutorial describes a simple method to construct a deep learning algorithm for performing 2-class sequence classification of metagenomic...
4.4K
Confirmation Biases01:31

Confirmation Biases

7.8K
The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
7.8K

You might also read

Related Articles

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

Sort by
Same author

Identify contaminants with decontam on the QIIME 2 Framework.

Microbiology resource announcements·2026
Same author

TrIdent - An R package to automate transductomics analysis of virus-like particle mediated DNA mobilization.

bioRxiv : the preprint server for biology·2026
Same author

Pathogenic bacterial species and the microbiome of cat fleas (Ctenocephalides felis) inhabiting flea-infested homes.

PloS one·2026
Same author

Guidelines for preventing and reporting contamination in low-biomass microbiome studies.

Nature microbiology·2025
Same author

Microbes with higher metabolic independence are enriched in human gut microbiomes under stress.

eLife·2025
Same author

Planning and describing a microbiome data analysis.

Nature microbiology·2025

Related Experiment Video

Updated: Jan 19, 2026

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples
11:23

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

Published on: December 22, 2014

37.6K

Consistent and correctable bias in metagenomic sequencing experiments.

Michael R McLaren1, Amy D Willis2, Benjamin J Callahan1,3

  • 1Department of Population Health and Pathobiology, North Carolina State University, Raleigh, United States.

Elife
|September 11, 2019
PubMed
Summary
This summary is machine-generated.

Experimental bias in marker-gene and metagenomic sequencing distorts biological community measurements. We developed a mathematical model to quantify and correct this bias, enabling more accurate and reproducible microbiome research.

Keywords:
16S rRNA genebiascalibrationcomputational biologyinfectious diseasemetagenomicsmicrobiologymicrobiomereproducibilitysystems biology

More Related Videos

Nanopore DNA Sequencing for Metagenomic Soil Analysis
07:33

Nanopore DNA Sequencing for Metagenomic Soil Analysis

Published on: December 14, 2017

31.6K
Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.6K

Related Experiment Videos

Last Updated: Jan 19, 2026

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples
11:23

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

Published on: December 22, 2014

37.6K
Nanopore DNA Sequencing for Metagenomic Soil Analysis
07:33

Nanopore DNA Sequencing for Metagenomic Soil Analysis

Published on: December 14, 2017

31.6K
Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.6K

Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Marker-gene and metagenomic sequencing are powerful tools for studying biological communities.
  • Current sequencing methods suffer from significant experimental bias, leading to inaccurate abundance measurements.
  • This bias hinders quantitative comparisons between different studies and protocols, potentially causing erroneous biological conclusions.

Purpose of the Study:

  • To develop a mathematical model that accurately describes how experimental bias distorts community measurements.
  • To validate the proposed model using real-world 16S rRNA gene and shotgun metagenomics data.
  • To demonstrate the utility of the model for evaluating sequencing protocols and correcting for bias.

Main Methods:

  • Development of a novel mathematical model for quantifying experimental bias in sequencing data.
  • Validation of the model using 16S rRNA gene sequencing data from defined bacterial communities.
  • Validation of the model using shotgun metagenomics data from defined bacterial communities.

Main Results:

  • The proposed mathematical model accurately fits experimental data from defined bacterial communities.
  • The model is simpler than previous approaches while providing a better fit.
  • The model effectively quantifies bias and can be used to evaluate different sequencing protocols.

Conclusions:

  • The developed mathematical model offers a pathway to more quantitative and reproducible metagenomic measurements.
  • Understanding and correcting for experimental bias is crucial for accurate microbiome analysis.
  • The model provides tools for bias evaluation, downstream statistical analysis interpretation, and bias correction.