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

RNA-seq03:21

RNA-seq

11.8K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
11.8K

You might also read

Related Articles

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

Sort by
Same author

Genomic evolution and climate related drivers of cholera surges in Dhaka Bangladesh between 1996 and 2024.

Communications medicine·2026
Same author

A Bait-and-Switch Strategy Links Phenotypes to Genes Coding for Polymer-Degrading Enzymes in Intact Microbiomes.

Microbial biotechnology·2026
Same author

Evaluation of a Novel Climate-Driven SIR Model for Cholera Prediction.

GeoHealth·2026
Same author

Developing Scenario-Based Strategies for Health, Climate, and Environmental Preparedness: The One Health, One Earth Approach.

GeoHealth·2026
Same author

Microbial Community multi-omic analysis of marsh sediment post crustacean shell compost enrichment: pathogen emergence and community response.

bioRxiv : the preprint server for biology·2026
Same author

Predictive intelligence for future vibriosis risk in the eastern United States employing Bayesian spatial modeling.

Applied and environmental microbiology·2026

Related Experiment Video

Updated: Jan 19, 2026

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

A simple binomial test for estimating sequencing errors in public repository 16S rRNA sequences.

Young-Gun Zo1, Rita R Colwell

  • 1Center of Marine Biotechnology, University of Maryland Biotechnology Institute, 701 E. Pratt Street, Baltimore, MD 21202, USA,

Journal of Microbiological Methods
|December 25, 2007
PubMed
Summary

This study introduces a new binomial model to detect sequencing errors in 16S rRNA gene sequences, improving accuracy for bacterial identification. The method helps assess sequence reliability in public databases.

More Related Videos

Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization
12:37

Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization

Published on: April 14, 2016

40.1K
Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons
10:24

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Published on: August 29, 2014

84.5K

Related Experiment Videos

Last Updated: Jan 19, 2026

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
Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization
12:37

Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization

Published on: April 14, 2016

40.1K
Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons
10:24

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Published on: August 29, 2014

84.5K

Area of Science:

  • Microbial genomics
  • Bioinformatics
  • Molecular evolution

Background:

  • Public sequence databases contain numerous sequencing errors, impacting downstream analyses.
  • Previous models for assessing sequence identity, like the double binomial model for indel-excluded similarity (S), had limitations.
  • 16S rRNA gene sequences, particularly in Vibrionaceae, exhibit variability in length and contain indels, necessitating refined error detection methods.

Purpose of the Study:

  • To develop a robust statistical model for quantifying sequencing errors in 16S rRNA gene sequences, incorporating indels.
  • To adapt existing models to better suit the characteristics of real-world sequence data, such as those from Vibrionaceae.
  • To provide a reliable method for testing sequence identity and assessing the quality of publicly available microbial genomic data.

Main Methods:

  • Derived a simple binomial model for the similarity coefficient (H), including indels, from a previously established double binomial model for S.
  • Validated the model's fit using empirical data from 16S rRNA sequences.
  • Employed the exact binomial test, utilizing pre-determined or bootstrapped probabilities of base matching, to estimate sequencing error rates between duplicated sequences.

Main Results:

  • The derived binomial model demonstrated a good fit to empirical data, effectively accounting for indels in similarity calculations.
  • The method allows for the determination of relative sequencing error levels in duplicated sequences.
  • A strategy was proposed to overcome the limitation of paired sequence requirements by focusing on conserved regions and using BLAST search hits.

Conclusions:

  • The developed binomial model provides an effective means to assess sequencing errors in 16S rRNA gene sequences, including those with indels.
  • This approach enhances the reliability of sequence identity testing and improves the quality assessment of data in public databases.
  • The method offers a valuable tool for microbial genomics research, aiding in accurate bacterial identification and phylogenetic analysis.