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Related Experiment Videos

Ancestral sequence alignment under optimal conditions.

Alexander K Hudek1, Daniel G Brown

  • 1School of Computer Science, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada. akhudek@cs.uwaterloo.ca

BMC Bioinformatics
|November 19, 2005
PubMed
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Aligning ancestral sequences is explored for multiple genome alignment. Parsimony and maximum likelihood methods were tested, but sum-of-pairs algorithms yielded superior alignment accuracy.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple genome alignment is crucial in bioinformatics.
  • Aligning two multiple alignments is a key subproblem.
  • Sum-of-pairs heuristic is common but lacks clear biological meaning and can be slow.

Purpose of the Study:

  • To explore the accuracy of aligning alignments using ancestral sequence alignment.
  • To compare maximum likelihood and parsimony for inferring ancestral sequences.
  • To investigate the impact of ambiguity in ancestral sequences on alignment accuracy.

Main Methods:

  • Simulated evolution on phylogenetic trees to generate synthetic sequence data.
  • Evaluated four ancestral sequence reconstruction and alignment methods: parsimony, maximum likelihood, ambiguous parsimony, and ambiguous maximum likelihood.

Related Experiment Videos

  • Compared ancestral alignment methods against two sum-of-pairs algorithms (ClustalW and Ma, Zhang, and Wang heuristic).

    • Main Results:

    • Allowing ambiguity in ancestral sequences did not improve multiple alignment accuracy.
    • Alignment success with ambiguous ancestral sequences was highly sensitive to gap open cost.
    • Maximum likelihood resulted in less accurate alignments than parsimony for ancestral sequence inference.

    Conclusions:

    • Ancestral sequence alignment with ambiguity is not beneficial for multiple genome alignment.
    • Sum-of-pairs algorithms outperform ancestral sequence alignment methods in accuracy.
    • Parsimony is a more effective method than maximum likelihood for inferring ancestral sequences in this context.