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

On the correlation between composition and site-specific evolutionary rate: implications for phylogenetic inference.

Vivek Gowri-Shankar1, Magnus Rattray

  • 1School of Computer Science, University of Manchester, Manchester M13 9PL, United Kingdom.

Molecular Biology and Evolution
|October 21, 2005
PubMed
Summary
This summary is machine-generated.

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

A cell atlas of the developing human outflow tract of the heart and its adult aortic valve derivatives.

eLife·2026
Same author

DeepSynBa: actionable drug combination prediction with complete dose-response profiles.

Bioinformatics (Oxford, England)·2026
Same author

metaAPA: a tool for integration of PolyA site predictions from single-cell and spatial transcriptomics.

Bioinformatics advances·2026
Same author

Integration of unpaired and heterogeneous clinical flow cytometry data.

iScience·2026
Same author

Glucocorticoid-dependence and independence of the circadian liver transcriptome.

Npj biological timing and sleep·2026
Same author

One-hot news: drug synergy models shortcut molecular features.

Bioinformatics (Oxford, England)·2026

Phylogenetic methods often assume uniform base composition, but spatial variation within genes can bias evolutionary estimates. A new Gaussian process model in PHASE accounts for this heterogeneity, improving phylogenetic accuracy for RNA sequences.

Area of Science:

  • Evolutionary Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Phylogenetic reconstruction methods commonly assume homogeneous nucleotide frequencies across sequence sites and lineages.
  • Biological sequences often exhibit compositional heterogeneity, both over time and spatially within genes.
  • Previous research has focused on temporal compositional variation, but the impact of spatial heterogeneity within genes on phylogenetic estimates is less understood.

Purpose of the Study:

  • To investigate the effects of spatial compositional heterogeneity within RNA genes on phylogenetic parameter estimation.
  • To challenge previous interpretations of ancestral sequence composition based on spatial heterogeneity.
  • To develop and implement a novel phylogenetic model that accounts for site-specific compositional variation.

Related Experiment Videos

Main Methods:

  • Analysis of nucleotide frequency trends across sites in RNA gene alignments.
  • Correlation of compositional patterns with site-specific evolutionary rates.
  • Development of a new phylogenetic model using a Gaussian process prior to model smooth compositional changes with evolutionary rate.
  • Implementation of the model in the PHASE software for Bayesian phylogenetic inference.

Main Results:

  • Spatial compositional heterogeneity exists within RNA gene alignments, often correlated with evolutionary rates.
  • Standard phylogenetic methods produce biased estimates of equilibrium frequencies, favoring fast-evolving sites.
  • Some existing methods produce biased ancestral composition estimates, favoring slow-evolving sites, which impacts interpretations of ancestral states like the Last Universal Ancestor.
  • The proposed Gaussian process model accurately captures observed compositional trends in contemporary RNA sequences.

Conclusions:

  • Spatial compositional heterogeneity significantly impacts phylogenetic reconstruction and parameter estimation.
  • The new Gaussian process model provides a more accurate approach to phylogenetic inference by accommodating site-specific compositional variation.
  • This model has implications for understanding evolutionary history, including the composition of early life forms.