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

From DNA to Protein03:06

From DNA to Protein

19.8K
The flow of genetic information in cells from DNA to mRNA to protein is described by the central dogma, which states that genes specify the sequence of mRNAs, which in turn specify the sequence of amino acids making up all proteins. The decoding of one molecule to another is performed by specific proteins and RNAs. Because the information stored in DNA is so central to cellular function, it makes intuitive sense that the cell would make mRNA copies of this information for protein synthesis...
19.8K
Genomic DNA in Prokaryotes00:46

Genomic DNA in Prokaryotes

45.4K
The genome of most prokaryotic organisms consists of double-stranded DNA organized into one circular chromosome in a region of cytoplasm called the nucleoid. The chromosome is tightly wound, or supercoiled, for efficient storage. Prokaryotes also contain other circular pieces of DNA called plasmids. These plasmids are smaller than the chromosome and often carry genes that confer adaptive functions, such as antibiotic resistance.
Genomic Diversity in Bacteria
Although bacterial genomes are much...
45.4K
Translation in Prokaryotes01:29

Translation in Prokaryotes

393
Prokaryote translation is a complex, highly coordinated process that converts genetic information from mRNA into functional proteins. It involves three stages: initiation, elongation, and termination, each facilitated by specific molecular components.Initiation of TranslationThe process begins with the assembly of the ribosomal subunits and initiation factors on the mRNA. In bacteria, the 30S ribosomal subunit recognizes the Shine-Dalgarno sequence in the mRNA, a conserved region upstream of...
393
Leaky Scanning02:28

Leaky Scanning

5.3K
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
5.3K
Improving Translational Accuracy02:07

Improving Translational Accuracy

12.0K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
12.0K
Prokaryotic Gene Structure and Organization01:28

Prokaryotic Gene Structure and Organization

975
Prokaryotic genomes exhibit a streamlined organization of coding and non-coding regions essential for gene expression and protein synthesis. While coding regions contain the genetic instructions for proteins or functional RNAs, non-coding regions regulate the precise transcription and translation of these genes.Coding Regions: Proteins and RNAsThe primary coding regions, known as structural genes, include sequences transcribed into messenger RNA (mRNA) and ultimately translated into...
975

You might also read

Related Articles

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

Sort by
Same author

Strain-level genomic analysis of <i>Staphylococcus epidermidis</i> across multiple body sites in healthy females.

Microbiology spectrum·2026
Same author

Shared taxa but distinct communities: within-individual comparisons of oral, nasal, and urinary microbiomes in asymptomatic "healthy" females.

Frontiers in microbiomes·2026
Same author

Diverse Temperate Coliphages of the Urinary Tract.

Viruses·2026
Same author

Draft genome of <i>Corynebacterium glycinophilum</i> S209 isolated from healthy human skin.

Microbiology resource announcements·2025
Same author

Draft genome sequences of <i>Staphylococcus</i> species isolated from urine samples from asymptomatic females.

Microbiology resource announcements·2025
Same author

Streptococcus anginosus of the urogenital tract: evidence of the same strain across anatomical sites of the same females.

BMC genomics·2025

Related Experiment Video

Updated: Oct 15, 2025

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers
10:41

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers

Published on: June 24, 2019

8.5K

The Dynamic Codon Biaser: calculating prokaryotic codon usage biases.

Brian Dehlinger1, Jared Jurss1, Karson Lychuk1

  • 1Bioinformatics Program, Loyola University Chicago, Chicago, IL 60660, USA.

Microbial Genomics
|October 26, 2021
PubMed
Summary
This summary is machine-generated.

The Dynamic Codon Biaser (DCB) tool offers dynamic calculation of bacterial codon usage bias. This tool aids in analyzing highly expressed genes (HEGs) for evolutionary and phage-host prediction studies.

Keywords:
Dynamic Codon Biasercodon biascodon usageprokaryotes

More Related Videos

Measurement of Specific Mycobacterial Mistranslation Rates with Gain-of-function Reporter Systems
06:18

Measurement of Specific Mycobacterial Mistranslation Rates with Gain-of-function Reporter Systems

Published on: April 26, 2019

6.1K
Quantification of the Abundance and Charging Levels of Transfer RNAs in Escherichia coli
10:34

Quantification of the Abundance and Charging Levels of Transfer RNAs in Escherichia coli

Published on: August 22, 2017

9.5K

Related Experiment Videos

Last Updated: Oct 15, 2025

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers
10:41

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers

Published on: June 24, 2019

8.5K
Measurement of Specific Mycobacterial Mistranslation Rates with Gain-of-function Reporter Systems
06:18

Measurement of Specific Mycobacterial Mistranslation Rates with Gain-of-function Reporter Systems

Published on: April 26, 2019

6.1K
Quantification of the Abundance and Charging Levels of Transfer RNAs in Escherichia coli
10:34

Quantification of the Abundance and Charging Levels of Transfer RNAs in Escherichia coli

Published on: August 22, 2017

9.5K

Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • Bacterial genomes exhibit codon usage bias, particularly in highly expressed genes.
  • This bias can be influenced by selection, mutation, and functional roles in gene expression and protein structure.
  • Existing tools for analyzing codon bias require updates due to the increasing volume of bacterial genome data.

Purpose of the Study:

  • To introduce the Dynamic Codon Biaser (DCB) tool, a web application for calculating prokaryotic codon usage bias statistics.
  • To provide a dynamic and accessible method for analyzing codon bias in bacterial genomes.
  • To facilitate downstream applications in evolutionary studies and phage-host predictions.

Main Methods:

  • The DCB tool calculates codon usage bias statistics for prokaryotic genomes.
  • Calculations are based on 40 highly conserved, highly expressed genes (HEGs) across prokaryotic species.
  • Users can input an NCBI accession number or upload their own sequence for analysis.

Main Results:

  • The DCB tool dynamically computes codon usage bias statistics.
  • It returns both the bias statistics and the corresponding HEG sequences for the analyzed genome.
  • The tool is freely available with its source code.

Conclusions:

  • The DCB tool provides a valuable resource for analyzing bacterial codon usage bias.
  • Its dynamic nature and focus on HEGs offer new insights for evolutionary and ecological studies.
  • The accessibility of the tool promotes wider research in bacterial genomics.