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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Phylogenetic Trees03:21

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Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
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Evolutionary Relationships through Genome Comparisons02:54

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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...
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Phylogeny01:23

Phylogeny

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Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.
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Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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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...
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Gene Evolution - Fast or Slow?02:05

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Updated: Nov 17, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

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Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic

George G Vega Yon1, Duncan C Thomas1, John Morrison1

  • 1Division of Biostatistics, Department of Preventive Medicine, University of Southern California, Los Angeles, California, United States of America.

Plos Computational Biology
|February 18, 2021
PubMed
Summary
This summary is machine-generated.

We developed a Bayesian computational model for predicting gene function evolution using phylogenies. This efficient method aids genetic data analysis by overcoming limitations of experimental characterization.

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Area of Science:

  • Genomics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Gene function annotation is crucial for genetic data analysis.
  • Experimental methods for gene function characterization are time-consuming and expensive.
  • Computational prediction of gene function is a vital research area.

Purpose of the Study:

  • To develop a computationally efficient Bayesian framework for estimating parameters of gene annotation evolution using phylogenies.
  • To address the challenge of implementing practical Bayesian methods for phylogenetic gene function prediction.

Main Methods:

  • Developed a Bayesian model for gene annotation evolution incorporating phylogenetic information.
  • Utilized Markov Chain Monte Carlo (MCMC) for efficient parameter estimation.
  • Applied the model to estimate parameters across diverse phylogenetic trees and gene functions.

Main Results:

  • The developed model efficiently estimates evolutionary parameters for gene annotations.
  • Estimated parameters align with biological expectations, such as increased function change post-gene duplication.
  • The method demonstrated strong performance in leave-one-out cross-validation analyses.

Conclusions:

  • The novel Bayesian phylogenetic framework provides an efficient computational tool for gene function prediction.
  • The model's ability to estimate parameters across various trees and functions enhances its applicability.
  • Validated predictions suggest the method's utility for guiding future experimental research in genomics.