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Parameterizing sequence alignment with an explicit evolutionary model.

Elena Rivas1, Sean R Eddy2,3,4,5

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New evolutionary models improve sequence homology inference by incorporating time-dependent evolutionary divergence. Optimized branch lengths enhance local alignment accuracy, reducing errors in identifying homologous sequences.

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Area of Science:

  • Bioinformatics and Computational Biology
  • Evolutionary Biology
  • Genomics and Proteomics

Background:

  • Sequence homology inference is crucial for understanding evolutionary relationships.
  • Current methods like BLAST and profile HMMs use fixed scoring systems, assuming uniform evolutionary divergence times.
  • This overlooks the time-dependent nature of evolutionary processes, particularly for insertions and deletions.

Purpose of the Study:

  • To develop and implement probabilistic evolutionary models compatible with affine-cost insertion/deletion models in sequence alignment.
  • To address the limitations of fixed scoring systems in reflecting realistic evolutionary divergence.
  • To improve the accuracy and coverage of homology detection in sequence alignments.

Main Methods:

  • Identification and implementation of probabilistic evolutionary models for pairwise sequence alignment.
  • Development of models compatible with symmetric pair HMMs (Smith-Waterman) and profile-based alignment.
  • Testing of 'optimized branch length' models on global and local homology benchmarks for alignment accuracy and coverage.

Main Results:

  • A single long branch parameterization proved sufficient for both distant and close global homologies.
  • Explicit evolutionary models demonstrated an advantage for local homology inference.
  • Optimized branch parameterization effectively reduced 'homologous overextension' in local alignments.

Conclusions:

  • Time-dependent evolutionary models offer significant improvements for local homology detection.
  • The 'optimized branch length' approach enhances the accuracy of identifying homologous subsequences.
  • This work provides a more realistic framework for inferring evolutionary relationships from sequence data.