title: Statistical studies of biomolecular sequences: score-based methods.
abstract: The massive accumulation of DNA and protein sequence data poses challenges and opportunities in terms of interpretation and analysis. This presentation reviews the method of score-based sequence analysis with the objectives of discerning distinctive segments in single sequences and identifying significant common segments in sequence comparisons. A number of new results are described here for both the theory and its applications. These include distributional theory involving several high scoring segments in single sequences, distribution formulas for general scoring regimes in multiple sequence comparisons, bounds for periodic scoring assignments, sensitivity analysis of genome composition and refinements on predicting exons and genes in DNA sequences.
authors: Department of Mathematics, Stanford University, California 94305-2125.
authors: S
authors: S
authors: Karlin
mesh_terms: D000595:Amino Acid Sequence; D001483:Base Sequence; D001699:Biometry; D004247:DNA / Q000737:chemistry* / Q000235:genetics; D005091:Exons; D005796:Genes; D016678:Genome; D006801:Humans; D011336:Probability; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D017386:Sequence Homology, Amino Acid; D012689:Sequence Homology, Nucleic Acid
chemical_list: D011506:Proteins; D004247:DNA