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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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A complexity-based measure and its application to phylogenetic analysis.

Xiaoqi Zheng1,2, Chun Li3, Jun Wang4,5

  • 11Department of Applied Mathematics, Dalian University of Technology, Dalian, 116024 People's Republic of China.

Journal of Mathematical Chemistry
|March 28, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces novel biological sequence distance metrics using complexity profiles. These metrics successfully reconstruct evolutionary relationships for mammals and coronaviruses, aligning with known taxonomy.

Keywords:
Phylogenetic analysisSARS-CoVSequence complexitymtDNA

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Phylogenetic analysis relies on accurate distance metrics for sequence data.
  • Existing metrics may not fully capture the complexity of biological sequences.

Purpose of the Study:

  • To develop and validate new distance metrics for biological sequences.
  • To apply these metrics to construct phylogenetic trees for Eutherian mammals and coronaviruses.

Main Methods:

  • Utilized a universal complexity profile to define two novel distance metrics.
  • Constructed phylogenetic trees by comparing mitochondrial DNA (mtDNA) sequences of mammals and whole genomes of coronaviruses.

Main Results:

  • Phylogenetic trees were generated for 18 Eutherian mammals and 24 coronaviruses.
  • The constructed trees formed monophyletic clusters that corresponded well with established taxonomic classifications.

Conclusions:

  • The proposed distance metrics are effective for phylogenetic analysis.
  • Complexity profiles offer a robust foundation for measuring biological sequence divergence and reconstructing evolutionary history.