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

Frequency-dependent Selection01:21

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...
Leaky Scanning02:28

Leaky Scanning

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 stands for...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

You might also read

Related Articles

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

Sort by
Same author

Chaperone regulation of biomolecular condensates.

Frontiers in biophysics·2026
Same author

Transcriptome-wide mRNP condensation precedes stress granule formation and excludes new mRNAs.

Molecular cell·2025
Same author

Translon: a single term for translated regions.

Nature methods·2025
Same author

The GATA-like transcription factor Gat201 determines alkaline-restricted growth in <i>Cryptococcus neoformans</i>.

mSphere·2025
Same author

The type of carbon source not the growth rate it supports can determine diauxie in Saccharomyces cerevisiae.

Communications biology·2025
Same author

Improved gene editing and fluorescent-protein tagging in <i>Aspergillus nidulans</i> using a Golden Gate-based CRISPR-Cas9 plasmid system.

Wellcome open research·2024
Same journal

The life history of recessive deleterious alleles as seen through the eyes of a honey bee (Apis mellifera).

Molecular biology and evolution·2026
Same journal

Severe bottleneck of ancient Homo populations: Insights from computational modeling and relevant fossil evidence.

Molecular biology and evolution·2026
Same journal

Population Epigenetics: Deciphering DNA Methylation Diversity and its Implications for Health, Disease, and Evolution.

Molecular biology and evolution·2026
Same journal

Genomic signature of repeated transitions to diurnality in spiders.

Molecular biology and evolution·2026
Same journal

Phylogenomic blind spots: The limits of UCE and BUSCO loci in the presence of gene flow.

Molecular biology and evolution·2026
Same journal

seqLens: Optimizing Language Models for Genomic Predictions.

Molecular biology and evolution·2026
See all related articles

Related Experiment Video

Updated: May 13, 2026

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

Estimating selection on synonymous codon usage from noisy experimental data.

Edward W J Wallace1, Edoardo M Airoldi, D Allan Drummond

  • 1Department of Biochemistry and Molecular Biology, University of Chicago, USA. ewallace@uchicago.edu

Molecular Biology and Evolution
|March 16, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new analytical framework to accurately estimate selection and mutation forces on codon usage by accounting for noise in gene expression data. This method provides more robust and higher estimates of selection strength in budding yeast.

Keywords:
codon usagegene expressionnoiseselection

More Related Videos

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

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

Related Experiment Videos

Last Updated: May 13, 2026

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

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

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

Area of Science:

  • Molecular Evolution
  • Population Genetics
  • Genomics

Background:

  • Understanding the forces shaping codon usage is crucial for molecular evolution.
  • Previous studies faced challenges in quantifying selection and mutation contributions due to gene expression measurement noise.
  • Translational efficiency and accuracy are key targets of selection, but their estimation is complicated by transcript abundance variability.

Purpose of the Study:

  • To develop a robust analytical framework for estimating selection and mutation strengths on codon usage.
  • To address the impact of measurement noise in gene expression data on evolutionary parameter estimates.
  • To provide a method for distinguishing biological signals from noise in gene expression-dependent analyses.

Main Methods:

  • Developed a population-genetic model incorporating explicit modeling of noise in gene expression data.
  • Applied the framework to analyze codon usage in budding yeast using various gene expression datasets.
  • Introduced per-gene selection estimates interpretable within an evolutionary context.

Main Results:

  • Gene expression noise significantly impacts estimates of selection and mutation, often leading to underestimation.
  • The new method yields robust and substantially higher estimates of mutation and selection strength compared to noise-blind approaches.
  • Per-gene selection estimates correlate with existing metrics like the codon adaptation index but offer a clearer evolutionary interpretation.
  • Observed a wide range of selection strengths for codon usage in budding yeast, from negligible to significant.

Conclusions:

  • Measurement noise in gene expression is a critical factor to consider in molecular evolution studies.
  • The developed analytical framework provides more accurate and reliable estimates of evolutionary forces.
  • This approach has broad applicability for analyzing gene expression data in various biological contexts.