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Frin: An Efficient Method for Representing Genome Evolutionary History.

Yan Hong1, Juan Wang1

  • 1School of Computer Science, Inner Mongolia University, Hohhot, China.

Frontiers in Genetics
|December 24, 2019
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Summary
This summary is machine-generated.

The Frin algorithm improves phylogenetic network construction by considering taxon frequency and incompatibility, producing simpler and more consistent evolutionary histories. This method offers a more robust representation of evolutionary data compared to previous approaches.

Keywords:
evolutionfrequencygenomeincompatibility degreephylogenetic network

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Area of Science:

  • Evolutionary Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Phylogenetic trees represent evolutionary history but have limitations in depicting complex evolutionary relationships.
  • Phylogenetic networks offer a more comprehensive way to visualize genome evolutionary histories.
  • The Cass algorithm is efficient for constructing phylogenetic networks but is sensitive to input data order.

Purpose of the Study:

  • To address the input order sensitivity of the Cass algorithm for phylogenetic network construction.
  • To develop an improved algorithm that generates simpler and more consistent phylogenetic networks.
  • To enhance the representation of complex evolutionary information.

Main Methods:

  • Proposed an improved Cass algorithm named Frin.
  • Frin utilizes taxon frequency and incompatibility degree for network construction.
  • Developed Frin as a Java software package.

Main Results:

  • Frin constructs simpler phylogenetic networks compared to other methods.
  • Frin generates more consistent networks across different input data orders.
  • Phylogenetic networks generated by Frin more accurately reflect original phylogenetic tree information.

Conclusions:

  • Frin offers an efficient and robust method for phylogenetic network construction.
  • The algorithm overcomes the input order dependency of the Cass algorithm.
  • Frin provides a valuable tool for understanding complex evolutionary histories.