title: Chance and statistical significance in protein and DNA sequence analysis.
abstract: Statistical approaches help in the determination of significant configurations in protein and nucleic acid sequence data. Three recent statistical methods are discussed: (i) score-based sequence analysis that provides a means for characterizing anomalies in local sequence text and for evaluating sequence comparisons; (ii) quantile distributions of amino acid usage that reveal general compositional biases in proteins and evolutionary relations; and (iii) r-scan statistics that can be applied to the analysis of spacings of sequence markers.
authors.affiliation: Department of Mathematics, Stanford University, CA 94305.
authors.forename: S
authors.initials: S
authors.lastname: Karlin
authors.forename: V
authors.initials: V
authors.lastname: Brendel
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D001412:Bacillus subtilis / Q000235:genetics; D001483:Base Sequence; D004247:DNA / Q000737:chemistry / Q000235:genetics*; D004330:Drosophila / Q000235:genetics; D004926:Escherichia coli / Q000235:genetics; D006801:Humans; D008433:Mathematics; D008957:Models, Genetic; D015233:Models, Statistical; D011506:Proteins / Q000737:chemistry / Q000235:genetics*; D012441:Saccharomyces cerevisiae / Q000235:genetics; D014780:Viruses / Q000235:genetics
chemical_list: D011506:Proteins; D004247:DNA