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A Practical Guide to Phylogenetics for Nonexperts
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PhyPA: Phylogenetic method with pairwise sequence alignment outperforms likelihood methods in phylogenetics involving

Xuhua Xia1

  • 1Department of Biology, University of Ottawa, 30 Marie Curie, Ottawa K1N 6N5, Canada; Ottawa Institute of Systems Biology, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada.

Molecular Phylogenetics and Evolution
|July 6, 2016
PubMed
Summary

Phylogenetic analysis using pairwise sequence alignment (PSA) is surprisingly more accurate for highly diverged sequences than multiple sequence alignment (MSA) methods. This new method, PhyPA, outperforms traditional maximum likelihood with MSA approaches for reconstructing evolutionary trees.

Keywords:
Codon-based alignmentDeep phylogenyDistance-based methodsLikelihood-based methodsSequence alignment

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Area of Science:

  • Computational Biology
  • Phylogenetics
  • Bioinformatics

Background:

  • Multiple sequence alignment (MSA) methods can produce poorly aligned sequences for highly divergent datasets, negatively impacting phylogenetic accuracy.
  • Traditional phylogenetic reconstruction methods relying on MSA often struggle with highly divergent sequences.
  • Pairwise sequence alignment (PSA) offers an alternative for distance-based phylogenetic reconstruction.

Purpose of the Study:

  • To compare the accuracy of a novel phylogenetics by pairwise alignment (PhyPA) method against maximum likelihood with MSA (ML+MSA) for reconstructing phylogenetic trees.
  • To evaluate the performance of PhyPA across nucleotide, amino acid, and codon sequences with varying evolutionary parameters.
  • To investigate the reasons behind the failure of ML+MSA to recover true topologies with simulated data.

Main Methods:

  • Developed and implemented PhyPA, a computational approach utilizing PSA for phylogenetic tree reconstruction.
  • Compared PhyPA against ML+MSA using simulated datasets with diverse topologies and tree lengths.
  • Analyzed nucleotide, amino acid, and codon sequences to assess method robustness.
  • Investigated the impact of MSA quality on phylogenetic signal recovery in ML+MSA.

Main Results:

  • PhyPA consistently outperformed ML+MSA for highly diverged sequences, even with optimized ML+MSA parameters.
  • ML+MSA only outperformed PhyPA when sequences were not highly diverged and MSA quality was high.
  • Maximum likelihood (ML) with true alignments recovered correct topologies, but ML+MSA with common alignment programs (MAFFT, MUSCLE) distorted the phylogenetic signal, leading to incorrect topology recovery.
  • The failure of ML+MSA was attributed to MSA distortion rather than insufficient tree search.

Conclusions:

  • PhyPA presents a more accurate and efficient method for phylogenetic reconstruction, particularly for highly divergent sequences.
  • MSA methods can introduce significant phylogenetic noise, hindering accurate evolutionary inference.
  • The study highlights the limitations of current MSA-based phylogenetic approaches and introduces a viable alternative.
  • PhyPA and multi-gene data analysis tools for phylogenetic support have been implemented in DAMBE.