title: Poisson, compound Poisson and process approximations for testing statistical significance in sequence comparisons.
abstract: DNA and protein sequence comparisons are performed by a number of computational algorithms. Most of these algorithms search for the alignment of two sequences that optimizes some alignment score. It is an important problem to assess the statistical significance of a given score. In this paper we use newly developed methods for Poisson approximation to derive estimates of the statistical significance of k-word matches on a diagonal of a sequence comparison. We require at least q of the k letters of the words to match where 0 less than q less than or equal to k. The distribution of the number of matches on a diagonal is approximated as well as the distribution of the order statistics of the sizes of clumps of matches on the diagonal. These methods provide an easily computed approximation of the distribution of the longest exact matching word between sequences. The methods are validated using comparisons of vertebrate and E. coli protein sequences. In addition, we compare two HLA class II transplantation antigens by this method and contrast the results with a dynamic programming approach. Several open problems are outlined in the last section.
authors.affiliation: Department of Mathematics, University of Southern California, Los Angeles 90089-1113.
authors.forename: L
authors.initials: L
authors.lastname: Goldstein
authors.forename: M S
authors.initials: MS
authors.lastname: Waterman
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D003627:Data Interpretation, Statistical; D008433:Mathematics; D008969:Molecular Sequence Data; D016012:Poisson Distribution; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000706:statistics & numerical data*
chemical_list: D011506:Proteins