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

New maximum likelihood estimators for eukaryotic intron evolution.

Hung D Nguyen1, Maki Yoshihama, Naoya Kenmochi

  • 1Frontier Science Research Center, University of Miyazaki, Kiyotake, Miyazaki, Japan.

Plos Computational Biology
|January 4, 2006
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

Altered translation efficiency of specific mRNAs in a zebrafish model of Diamond-Blackfan anemia syndrome.

Biochemical and biophysical research communications·2026
Same author

Landscape of driver mutations and their clinical effects on Down syndrome-related myeloid neoplasms.

Blood·2024
Same author

A stochastic B cell affinity maturation model to characterize mechanisms of protection for tetravalent dengue vaccine constructs.

Frontiers in molecular biosciences·2023
Same author

Sr doped TiO<sub>2</sub> photocatalyst for the removal of Janus Green B dye under visible light.

RSC advances·2023
Same author

Phosphorylation of α-synuclein at T64 results in distinct oligomers and exerts toxicity in models of Parkinson's disease.

Proceedings of the National Academy of Sciences of the United States of America·2023
Same author

A static and dynamic copula-based ARIMA-fGARCH approach to determinants of carbon dioxide emissions in Argentina.

Environmental science and pollution research international·2022
Same journal

Another 10 years of PLOS Computational Biology: A data-driven reflection on trends in genomics research.

PLoS computational biology·2026
Same journal

Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.

PLoS computational biology·2026
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
See all related articles

Spliceosomal intron evolution is clarified by a new method estimating intron gain and loss rates. This research reveals more intron gains than losses, aiding understanding of genomic evolution.

Area of Science:

  • Genetics
  • Molecular Biology
  • Evolutionary Biology

Background:

  • The evolutionary history of spliceosomal introns is a complex and debated topic.
  • Previous methods for inferring intron evolution from conserved positions have yielded conflicting results.

Purpose of the Study:

  • To develop and apply a novel maximum likelihood method to estimate intron insertion target site frequency and rates of intron gain and loss.
  • To resolve long-standing questions regarding the evolution of spliceosomal introns.

Main Methods:

  • Analysis of 10,044 introns across 7,221 intron positions in conserved regions of 684 orthologous sets from seven eukaryotes.
  • Application of a novel maximum likelihood model to quantify intron dynamics.

Main Results:

Related Experiment Videos

  • Identified an average of one intron insertion target site per 11.86 base pairs.
  • Determined that intron gains exceed intron losses by approximately 25%, with variations across time and lineages.
  • Quantified parallel intron gains (18.5%) and reacquisition events (0.5%).

Conclusions:

  • The developed method provides a robust framework for studying intron evolution.
  • The findings offer critical insights into the dynamics of intron gain and loss, contributing to a clearer understanding of spliceosomal intron evolution.