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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

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Published on: August 14, 2018

Live phylogeny.

Guilherme P Telles1, Nalvo F Almeida, Rosane Minghim

  • 1Institute of Computing, University of Campinas, Campinas, Brazil. gpt@ic.unicamp.br

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|January 9, 2013
PubMed
Summary
This summary is machine-generated.

The live phylogeny problem extends traditional phylogenetics to include living ancestors, applicable to fast-evolving viruses and non-biological data. This study introduces new algorithms and explores computational complexity for this generalized problem.

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Area of Science:

  • Computational Biology
  • Phylogenetics
  • Data Science

Background:

  • Traditional phylogeny construction assumes distinct, non-living ancestors.
  • Fast-evolving species (e.g., viruses) and non-biological data (e.g., documents) challenge these assumptions.
  • A generalized approach is needed to accommodate 'living' or co-existing ancestors.

Purpose of the Study:

  • To formalize the live phylogeny problem for distance-based and character-state data.
  • To develop efficient algorithms for specific instances of the live phylogeny problem.
  • To investigate the computational complexity of generalized live phylogeny.

Main Methods:

  • Formalization of the live phylogeny problem using distance matrices.
  • Formalization of the live phylogeny problem using character state data.
  • Development of polynomial-time algorithms for restricted versions of the problem.

Main Results:

  • The live phylogeny problem is formally defined for distance and character state data.
  • Polynomial-time algorithms are presented for specific cases of the live phylogeny problem.
  • The study posits that more general versions are likely NP-hard.

Conclusions:

  • The live phylogeny problem offers a more realistic model for certain biological and non-biological datasets.
  • Algorithmic solutions for specific live phylogeny problems have been developed.
  • Future research directions include heuristic and approximation algorithms for NP-hard versions.