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Area of Science:

  • Evolutionary biology
  • Bioinformatics
  • Computational biology

Background:

  • Accurate ancestral sequence reconstruction (ASR) is vital for studying ancient proteins and comparing species.
  • Multiple sequence alignment (MSA) is fundamental to ASR but can introduce biases.
  • The impact of different MSA methodologies on ASR accuracy remains largely unknown.

Purpose of the Study:

  • To investigate how various multiple sequence alignment (MSA) methodologies influence ancestral sequence reconstruction (ASR) accuracy.
  • To evaluate the impact of different alignment algorithms and evolutionary parameters on ASR outcomes.
  • To identify MSA methods that provide the most reliable ancestral sequence reconstructions.

Main Methods:

  • A simulation study was conducted using diverse evolutionary scenarios.
  • Ancestral protein sequence reconstruction accuracy was evaluated using different MSA methods.
  • Reconstruction outcomes were compared across various alignment algorithms and parameters.

Main Results:

  • MSA methodology, aligner algorithms, tree topology, and insertion/deletion rates significantly impact ASR accuracy.
  • MAFFT consistency aligners and PRANK variants generally performed best, while FSA showed limited performance.
  • A bias towards inferring longer reconstructed sequences than true ancestors was observed across most MSA methods.
  • Measures of MSA quality strongly correlated with ASR accuracy.

Conclusions:

  • Differences in MSA methodology can substantially affect the quality and accuracy of ancestral sequence reconstructions.
  • Selecting appropriate MSA methods with care is essential for confident and accurate determination of ancestral states.
  • Future ASR studies should consider the influence of MSA choices on evolutionary inferences.