title: Making sense of score statistics for sequence alignments.
abstract: The search for similarity between two biological sequences lies at the core of many applications in bioinformatics. This paper aims to highlight a few of the principles that should be kept in mind when evaluating the statistical significance of alignments between sequences. The extreme value distribution is first introduced, which in most cases describes the distribution of alignment scores between a query and a database. The effects of the similarity matrix and gap penalty values on the score distribution are then examined, and it is shown that the alignment statistics can undergo an abrupt phase transition. A few types of random sequence databases used in the estimation of statistical significance are presented, and the statistics employed by the BLAST, FASTA and PRSS programs are compared. Finally the different strategies used to assess the statistical significance of the matches produced by profiles and hidden Markov models are presented.
authors.affiliation: Swiss Institute of Bioinformatics.
authors.forename: M
authors.initials: M
authors.lastname: Pagni
authors.forename: C V
authors.initials: CV
authors.lastname: Jongeneel
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D019295:Computational Biology; D016208:Databases, Factual; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D008969:Molecular Sequence Data; D011506:Proteins / Q000235:genetics; D016415:Sequence Alignment / Q000706:statistics & numerical data*; D017386:Sequence Homology, Amino Acid
chemical_list: D011506:Proteins