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 Experiment Videos

A Bayesian model comparison approach to inferring positive selection.

K Scheffler1, C Seoighe

  • 1Computational Biology Group, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Rondebosch, South Africa. konrad@cbio.uct.ac.za

Molecular Biology and Evolution
|August 27, 2005
PubMed
Summary

A new Bayesian approach improves the detection of positive selection in DNA sequences, especially with limited data. This method offers more accurate inference than traditional empirical Bayes methods, reducing false positives in evolutionary analysis.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Transceiver 16-Channel Coaxial-End Dipole Array for Combined Head and C-Spine MRI at 9.4 T.

NMR in biomedicine·2026
Same author

SMAS: Structural MRI-based AD Score using Bayesian supervised VAE.

Computers in biology and medicine·2025
Same author

An array of paired folded-end dipoles for whole-brain imaging at 9.4 T.

Journal of magnetic resonance (San Diego, Calif. : 1997)·2024
Same author

Evaluation of coaxial dipole antennas as transceiver elements of human head array for ultra-high field MRI at 9.4T.

Magnetic resonance in medicine·2023
Same author

Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation.

Medical image analysis·2023
Same author

MRzero - Automated discovery of MRI sequences using supervised learning.

Magnetic resonance in medicine·2021

Area of Science:

  • Evolutionary Biology
  • Molecular Evolution
  • Bioinformatics

Background:

  • Detecting positive selection commonly uses probabilistic models of codon evolution and maximum likelihood estimation.
  • This maximum likelihood approach is robust with large datasets but can be inaccurate with small datasets or low sequence divergence due to parameter uncertainties.
  • Errors in inference can arise from insufficient data or limited evolutionary distance between sequences.

Purpose of the Study:

  • To introduce and evaluate a Bayesian model comparison approach for inferring positive selection in DNA sequences.
  • To compare the performance of this Bayesian method against the empirical Bayes approach for site-specific positive selection inference.
  • To investigate the impact of phylogenetic tree length on the accuracy of both Bayesian and empirical Bayes methods.

Related Experiment Videos

Main Methods:

  • Developed a Bayesian model comparison framework to assess positive selection across entire sequences.
  • Integrated this framework into a Bayesian approach for site-specific inference of positive selection.
  • Utilized simulated sequences to compare the novel Bayesian method with the empirical Bayes approach, analyzing performance across varying tree lengths and divergence levels.

Main Results:

  • The Bayesian approach demonstrated superior performance compared to the empirical Bayes method, particularly when sequence divergence was small.
  • The Bayesian method showed reduced susceptibility to false-positive inferences in cases of sequence saturation.
  • Performance of both methods was comparable at intermediate levels of sequence divergence.

Conclusions:

  • The proposed Bayesian model comparison approach offers a more robust and accurate method for detecting positive selection, especially in challenging scenarios with limited data.
  • This Bayesian framework enhances the reliability of evolutionary inference by mitigating errors associated with small datasets and sequence saturation.
  • The findings suggest a shift towards Bayesian methods for improved accuracy in evolutionary studies of positive selection.