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08:57
Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
Published on: August 14, 2018
Statistical power of phylo-HMM for evolutionarily conserved element detection.
Xiaodan Fan1, Jun Zhu, Eric E Schadt
1Department of Statistics, Harvard University, Boston, MA, USA. xfan@fas.harvard.edu
BMC Bioinformatics
|October 9, 2007
Summary
This study evaluates the phylogenetic hidden Markov model (phylo-HMM) for identifying conserved elements in genomes. Its power depends significantly on the number of species and evolutionary distances, guiding comparative genomics study design.
Area of Science:
- Comparative genomics
- Bioinformatics
- Evolutionary biology
Background:
- Identifying functional elements via conservation analysis is crucial in comparative genomics.
- Phylogenetic hidden Markov models (phylo-HMM) offer a method for detecting conserved elements using multiple genome alignments.
- Rigorous evaluation of the phylo-HMM method's performance has been lacking.
Purpose of the Study:
- To investigate the statistical power of the phylo-HMM approach for detecting conserved elements.
- To identify key factors influencing the performance of phylo-HMM in comparative genomics.
- To provide guidelines for optimizing the selection of genomes and evolutionary distances in conservation analysis.
Main Methods:
- A simulation study was conducted to assess the power of the phylo-HMM method.
- Various factors, including the number of species, evolutionary distances, conservation ratio, and element length, were analyzed.
- The influence of genome topology and nucleotide substitution models was also examined.
Main Results:
- The power of phylo-HMM is highly dependent on the number of species genomes and their evolutionary distances.
- Conservation ratio and expected conserved element length are also significant factors influencing detection power.
- Genome topology and nucleotide substitution models have a minor impact on phylo-HMM performance.
Conclusions:
- The findings offer practical guidelines for selecting appropriate numbers of genomes and evolutionary distances in comparative genomics.
- Researchers can use these results to anticipate the power of phylo-HMM under different parameter settings.
- This work aids in the effective application of phylo-HMM for functional element identification.
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