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A probabilistic version of Sankoff's maximum parsimony algorithm.

Gábor Balogh1, Stephan H Bernhart1, Peter F Stadler2,3,4,5,6

  • 1Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, University Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany.

Journal of Bioinformatics and Computational Biology
|April 28, 2020
PubMed
Summary
This summary is machine-generated.

Gene copy number evolution is analyzed using a new probabilistic model. This method refines gene family history inference by accounting for suboptimal gene duplication and loss events.

Keywords:
Sankoff’s parsimony algorithmePoPEgene family evolutionmicroRNA evolutionpartition function

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Area of Science:

  • Evolutionary biology
  • Genomics
  • Bioinformatics

Background:

  • Gene family size fluctuates due to gene duplication and loss.
  • Accurate inference of copy number changes is crucial for evolutionary analysis.
  • Phylogenetic methods like maximum parsimony are used to infer these changes.

Purpose of the Study:

  • To develop a probabilistic model for inferring gene copy number changes along phylogenetic trees.
  • To improve upon traditional maximum parsimony methods by considering suboptimal assignments.
  • To reanalyze microRNA family gain and loss patterns in metazoans.

Main Methods:

  • Developed a probabilistic model, a partition function variant of Sankoff's parsimony algorithm.
  • Applied the model to infer gene copy number changes on a phylogenetic tree.
  • Compared results with the standard maximum parsimony approach.

Main Results:

  • The probabilistic model and maximum parsimony yield similar results when deviations from parsimony are minimal.
  • The probabilistic approach systematically predicts fewer gene gains and more gene losses.
  • Identified co-optimal solutions where parsimony selects biased outcomes.

Conclusions:

  • Probabilistic modeling offers a more nuanced view of gene family evolution than strict parsimony.
  • The refined method provides a more accurate reconstruction of gene gain and loss histories.
  • This approach enhances the understanding of microRNA family evolution in metazoans.