Chance and statistical significance in protein and DNA sequence analysis.
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.
Karlin
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S
Department of Mathematics, Stanford University, CA 94305.
Brendel
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V
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
D011506:Proteins; D004247:DNA