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The Relative Importance of Modeling Site Pattern Heterogeneity Versus Partition-Wise Heterotachy in Phylogenomic

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Site-heterogeneous models are crucial for accurate phylogenetic inference, outperforming partitioned models in most cases. Complete protein-wise partitioning should be avoided due to long-branch attraction bias, but clustering proteins can help mitigate this.

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Area of Science:

  • Evolutionary biology
  • Phylogenetics
  • Genomics

Background:

  • Accurate phylogenetic inference requires realistic models accounting for substitution pattern heterogeneity.
  • Site-heterogeneous and partitioned models are commonly used but their relative importance is unevaluated.

Purpose of the Study:

  • Compare the performance of partitioned, site-heterogeneous, and combined models in phylogenetic inference.
  • Evaluate the impact of different modeling strategies on resolving ancient phylogenetic relationships.

Main Methods:

  • Empirical analyses and large-scale simulations were conducted.
  • Compared site-homogeneous, site-heterogeneous, and partitioned models.
  • Investigated the effect of protein-wise partitioning and protein clustering (PartitionFinder).

Main Results:

  • Site-heterogeneous models generally outperformed site-homogeneous models, except in extreme protein-wise heterotachy scenarios.
  • Protein-wise partitioning led to long-branch attraction (LBA) bias due to compounded small sample bias.
  • Clustering similarly-evolving proteins improved phylogenetic accuracy in some cases.
  • Empirical data showed site-heterogeneous models correctly resolved the Microsporidia+Fungi grouping, unlike site-homogeneous models.

Conclusions:

  • Modeling site-heterogeneity is generally more important than protein-wise heterotachy for accurate phylogenomic analyses.
  • Avoid complete protein-wise partitioning to prevent LBA bias.
  • Clustering proteins combined with site-heterogeneous models is effective when extreme protein-wise heterotachy is present.