title: Island method for estimating the statistical significance of profile-profile alignment scores.
abstract: BACKGROUND
  In the last decade, a significant improvement in detecting remote similarity between protein sequences has been made by utilizing alignment profiles in place of amino-acid strings. Unfortunately, no analytical theory is available for estimating the significance of a gapped alignment of two profiles. Many experiments suggest that the distribution of local profile-profile alignment scores is of the Gumbel form. However, estimating distribution parameters by random simulations turns out to be computationally very expensive.
  
  
  RESULTS
  We demonstrate that the background distribution of profile-profile alignment scores heavily depends on profiles' composition and thus the distribution parameters must be estimated independently, for each pair of profiles of interest. We also show that accurate estimates of statistical parameters can be obtained using the "island statistics" for profile-profile alignments.
  
  
  CONCLUSIONS
  The island statistics can be generalized to profile-profile alignments to provide an efficient method for the alignment score normalization. Since multiple island scores can be extracted from a single comparison of two profiles, the island method has a clear speed advantage over the direct shuffling method for comparable accuracy in parameter estimates.
authors.affiliation: Department of Computer Science, University of Northern Iowa, Cedar Falls, IA 50614, USA. poleksic@cs.uni.edu
authors.forename: Aleksandar
authors.initials: A
authors.lastname: Poleksic
mesh_terms: D019295:Computational Biology / Q000379:methods*; D015233:Models, Statistical; D011506:Proteins / Q000737:chemistry; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods
chemical_list: D011506:Proteins