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Relative model selection of evolutionary substitution models can be sensitive to multiple sequence alignment

Stephanie J Spielman1, Molly L Miraglia2,3

  • 1Department of Biological Sciences, Rowan University, Glassboro, NJ, 08028, USA. spielman@rowan.edu.

BMC Ecology and Evolution
|November 30, 2021
PubMed
Summary

Multiple sequence alignment (MSA) uncertainty can impact evolutionary model selection in phylogenetics. While often robust, distinct best-fitting models may be chosen from different MSAs, especially for nucleotide data.

Keywords:
Models of sequence evolutionMultiple sequence alignmentPhylogeneticsRelative model selection

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Multiple sequence alignments (MSAs) are foundational for comparative sequence analysis and phylogenetics.
  • Errors in MSA construction can propagate to downstream analyses like phylogenetic reconstruction and divergence time estimation.
  • The impact of MSA uncertainty on relative model selection has not been previously investigated.

Purpose of the Study:

  • To assess the consistency of relative model selection when faced with uncertainty in multiple sequence alignments (MSAs).
  • To determine if MSA uncertainty influences the choice of evolutionary models in phylogenetic analyses.

Main Methods:

  • Perturbing a given MSA to create multiple versions.
  • Applying relative model selection to each perturbed MSA.
  • Comparing the best-fitting models selected from different MSAs derived from the same sequence set.

Main Results:

  • Relative model selection is generally robust to MSA uncertainty but can identify distinct best-fitting models in a significant number of cases.
  • This issue is more pronounced for nucleotide data than for amino-acid data.
  • Predicting the sensitivity of model selection to MSA uncertainty is challenging.

Conclusions:

  • MSA uncertainty can affect multiple stages of phylogenetic analysis pipelines more than previously understood.
  • Relative model selection, a crucial step, is susceptible to MSA uncertainty.
  • The findings highlight the need to consider MSA uncertainty in phylogenetic workflows.