title: Convergent Island Statistics: a fast method for determining local alignment score significance.
abstract: BACKGROUND
  Background distribution statistics for profile-based sequence alignment algorithms cannot be calculated analytically, and hence such algorithms must resort to measuring the significance of an alignment score by assessing its location among a distribution of background alignment scores. The Gumbel parameters that describe this background distribution are usually pre-computed for a limited number of scoring systems, gap schemes, and sequence lengths and compositions. The use of such look-ups is known to introduce errors, which compromise the significance assessment of a remote homology relationship. One solution is to estimate the background distribution for each pair of interest by generating a large number of sequence shuffles and use the distribution of their scores to approximate the parameters of the underlying extreme value distribution. This is computationally very expensive, as a large number of shuffles are needed to precisely estimate the score statistics.
  
  
  RESULTS
  Convergent Island Statistics (CIS) is a computationally efficient solution to the problem of calculating the Gumbel distribution parameters for an arbitrary pair of sequences and an arbitrary set of gap and scoring schemes. The basic idea behind our method is to recognize the lack of similarity for any pair of sequences early in the shuffling process and thus save on the search time. The method is particularly useful in the context of profile-profile alignment algorithms where the normalization of alignment scores has traditionally been a challenging task.
  
  
  BACKGROUND
  aleksandar@eidogen.com
  
  
  BACKGROUND
  http://www.eidogen-sertanty.com/Documents/convergent_island_stats_sup.pdf.
authors.affiliation: Eidogen-Sertanty Inc., 9381 Judicial Dr., San Diego, CA 92121, USA. aleksandar@eidogen.com
authors.forename: Aleksandar
authors.initials: A
authors.lastname: Poleksic
authors.forename: Joseph F
authors.initials: JF
authors.lastname: Danzer
authors.forename: Kevin
authors.initials: K
authors.lastname: Hambly
authors.forename: Derek A
authors.initials: DA
authors.lastname: Debe
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D008969:Molecular Sequence Data; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid