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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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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Improving phylogenetic analyses by incorporating additional information from genetic sequence databases.

Li-Jung Liang1, Robert E Weiss, Benjamin Redelings

  • 1Department of Medicine Statistics Core, UCLA School of Public Health, Los Angeles, CA 90095, USA. liangl@ucla.edu

Bioinformatics (Oxford, England)
|August 8, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian method to reduce uncertainty in phylogenetic analyses by creating informative priors from additional datasets. This approach enhances parameter estimation precision for evolutionary studies.

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Area of Science:

  • Evolutionary Biology
  • Computational Biology
  • Statistical Phylogenetics

Background:

  • Phylogenetic analyses often yield uncertain parameter estimates due to limited data.
  • Existing phylogenetic software defaults to vague priors, increasing uncertainty.
  • Informative priors can reduce posterior uncertainty in Bayesian phylogenetic analyses.

Purpose of the Study:

  • To develop principled statistical methods for more precise parameter estimation in phylogenetic analyses.
  • To create objective and informative priors using hierarchical random effect models.
  • To combine diverse datasets for improved phylogenetic inference.

Main Methods:

  • Constructing a fully Bayesian semiparametric hierarchical model to integrate datasets.
  • Utilizing additional sequence data from internal or public sources.
  • Employing a dynamic iteratively reweighted Markov chain Monte Carlo algorithm for sample recycling.

Main Results:

  • Demonstrated a method to generate informative priors for phylogenetic parameters.
  • Successfully applied the approach to analyze insertion-deletion (indel) processes in the enolase gene.
  • Incorporated prior information from 82 curated alignments from the BAliBASE database.

Conclusions:

  • The proposed method significantly enhances parameter estimation precision in phylogenetic analyses.
  • Hierarchical models provide an effective framework for integrating information across datasets.
  • This approach offers a valuable tool for advancing evolutionary studies and understanding molecular evolution.