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Related Experiment Videos

Joint Bayesian estimation of alignment and phylogeny.

Benjamin D Redelings1, Marc A Suchard

  • 1Department of Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1766, USA.

Systematic Biology
|July 14, 2005
PubMed
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This study introduces a new method for simultaneously estimating molecular sequence alignments and phylogenetic trees, accounting for alignment uncertainty. This approach improves accuracy by considering all possible alignments, unlike traditional methods.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Phylogenetic tree estimation traditionally relies on a single sequence alignment.
  • Existing methods often introduce bias due to reliance on guide trees in progressive alignment.
  • Alignment uncertainty is a significant factor not adequately addressed in current phylogenetic analyses.

Purpose of the Study:

  • To develop a novel model and algorithm for simultaneous estimation of molecular sequence alignments and phylogenetic trees.
  • To incorporate alignment uncertainty into phylogenetic inference.
  • To overcome limitations of progressive alignment methods and guide tree biases.

Main Methods:

  • Simultaneous estimation of alignments and phylogenetic trees, considering all possible alignments.

Related Experiment Videos

  • Joint modeling of alignment and phylogeny to avoid guide tree dependency.
  • Incorporation of site-rate variation and a novel indel model with affine gap penalties.
  • Application of Markov chain Monte Carlo (MCMC) methods with an improved transition kernel for efficient sampling and convergence.

    • Main Results:

    • Simultaneous estimation effectively accounts for alignment uncertainty.
    • The joint approach models site-rate variation and utilizes shared insertion/deletions (indels) for improved phylogenetic grouping.
    • The novel MCMC transition kernel enhances mixing efficiency, ensuring convergence even with arbitrary starting alignments.
    • Software implementation provides estimation of alignment uncertainty and visualization methods.

    Conclusions:

    • The novel joint estimation method offers a more robust approach to molecular sequence alignment and phylogenetic tree reconstruction.
    • Accounting for alignment uncertainty leads to more accurate phylogenetic inferences.
    • The developed algorithm and software provide valuable tools for evolutionary and computational biology research.