DNA assembly with gaps (Dawg): Simulating sequence evolution

Research output: Contribution to journalArticlepeer-review

98 Scopus citations


Motivation: Relationships amongst taxa are inferred from biological data using phylogenetic methods and procedures. Very few known phylogenies exist against which to test the accuracy of our inferences. Therefore, in the absence of biological data, simulated data must be used to test the accuracy of methods which produce these inferences. Researchers have limited or non-existent options for simulations useful for studying the impact of insertions, deletions, and alignments on phylogenetic accuracy. Results: To satisfy this gap I have developed a new algorithm of indel formation and incorporated it into a new, flexible, and portable application for sequence simulation. The application, called Dawg, simulates phylogenetic evolution of DNA sequences in continuous time using the robust general time reversible model with gamma and invariant rate heterogeneity and a novel length-dependent model of indel formation. On completion, Dawg produces the true alignment of the simulated sequences. Unlike other applications, Dawg allows indel lengths to be explicitly distributed via a biologically realistic power law. Many options are available to allow users to customize their simulations and results. Because simulating with indels would be problematic if biologically realistic parameters could not be estimated, a script is provided with Dawg that can estimate the parameters of indel formation from sequence data. Dawg was applied to the sequences of four chloroplast trnK introns. It was used to parametrically bootstrap an estimation of the rate of indel formation for the phylogeny. Because Dawg can assist in parametric bootstrapping of sequence data it is useful beyond phylogenetics, such as studying alignment algorithms or parameters of molecular evolution.

Original languageEnglish (US)
Pages (from-to)iii31-iii38
Issue numberSUPPL. 3
StatePublished - Nov 2005
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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