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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Published on: August 14, 2018

Local search for the generalized tree alignment problem.

Andrés Varón1, Ward C Wheeler

  • 1Division of Invertebrate Zoology, American Museum of Natural History, New York, NY 10024, USA.

BMC Bioinformatics
|February 28, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces new local search heuristics for the Generalized Tree Alignment Problem (GTAP), significantly improving computational efficiency for phylogenetic analysis. These methods enhance the speed of estimating evolutionary relationships from unaligned biological sequences.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Phylogenies represent organismal ancestry as binary trees, crucial for understanding evolutionary relationships.
  • The Generalized Tree Alignment Problem (GTAP) addresses simultaneous tree and alignment estimation under Maximum Parsimony, a computationally challenging NP-Hard problem.
  • GTAP is analogous to the Steiner Tree Problem, often analyzed using Manhattan or Hamming distances.

Purpose of the Study:

  • To implement and experimentally evaluate existing and novel local search heuristics for the GTAP.
  • To assess the performance of GTAP-based phylogenetic inference from unaligned sequences.
  • To improve the execution time of phylogenetic tree and alignment estimation.

Main Methods:

  • Development and application of new local search heuristics for the GTAP.
  • Experimental evaluation using simulated and real biological sequence data.
  • Comparison with existing methods for simultaneous tree and alignment estimation.

Main Results:

  • Phylogenies inferred using GTAP from unaligned sequences demonstrate competitive accuracy.
  • The implemented local search heuristics were tested on both simulated and real datasets.
  • The new heuristics were found to be effective in exploring the solution space for GTAP.

Conclusions:

  • The presented methods offer a substantial improvement in execution time, exceeding three orders of magnitude faster than previous best heuristics on real data.
  • These advancements make phylogenetic inference from unaligned sequences more computationally feasible.
  • The study highlights the practical utility of improved GTAP heuristics in evolutionary biology.