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

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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...
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...
Gene Families01:57

Gene Families

Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...
Gene Families01:57

Gene Families

Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).

You might also read

Related Articles

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

Sort by
Same author

Oligomer-dependent and -independent chaperone activity of sHsps in different stressed conditions.

FEBS open bio·2015
Same author

[Changes in portal vein and hepatic vein blood flow volume and their ratio in SD rats during induced carcinogenesis of hepatocellular carcinoma].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University·2015
Same author

Modeling the relationship of epigenetic modifications to transcription factor binding.

Nucleic acids research·2015
Same author

Artesunate induces apoptosis and inhibits growth of Eca109 and Ec9706 human esophageal cancer cell lines in vitro and in vivo.

Molecular medicine reports·2015
Same author

Chronopharmacodynamics and mechanisms of antitumor effect induced by erlotinib in xenograft-bearing nude mice.

Biochemical and biophysical research communications·2015
Same author

Characterization of protein alterations in damaged axons in the brainstem following traumatic brain injury using fourier transform infrared microspectroscopy: a preliminary study.

Journal of forensic sciences·2015
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: May 28, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

A Bayesian model for gene family evolution.

Liang Liu1, Lili Yu, Venugopal Kalavacharla

  • 1Department of Statistics, University of Georgia, Athens, GA 30602, USA. lliu@uga.edu

BMC Bioinformatics
|November 3, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian approach for gene family evolution modeling, offering more accurate parameter estimates than maximum likelihood methods. The new method also improves the identification of gene families that deviate from expected evolutionary patterns.

More Related Videos

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Related Experiment Videos

Last Updated: May 28, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Area of Science:

  • Evolutionary Biology
  • Computational Biology
  • Genomics

Background:

  • Gene family size evolution is often modeled using birth and death processes on phylogenetic trees.
  • Maximum likelihood methods are established for estimating birth/death rates and ancient gene family sizes.
  • Existing methods have limitations in accurately estimating evolutionary parameters.

Purpose of the Study:

  • To develop a Bayesian approach for estimating parameters in the birth and death model for gene family evolution.
  • To introduce a Bayesian hypothesis test for identifying gene families with unusual evolutionary trajectories.
  • To compare the performance of Bayesian and maximum likelihood methods.

Main Methods:

  • Developed a Bayesian framework for birth and death model parameter estimation.
  • Implemented a Bayesian hypothesis test using posterior p-values.
  • Applied methods to simulated data and a real dataset of yeast gene families.

Main Results:

  • Bayesian estimates of birth and death rates were more accurate than maximum likelihood estimates in simulations.
  • A constant rate model inadequately explained yeast gene family size variation.
  • A heterogeneous rate model revealed different unusual gene families compared to maximum likelihood.

Conclusions:

  • The Bayesian approach provides more accurate parameter estimation for gene family evolution.
  • Bayesian hypothesis testing effectively identifies gene families with non-standard evolutionary patterns.
  • This method enhances the understanding of gene family evolutionary dynamics across genomes.