title: Methods and algorithms for statistical analysis of protein sequences.
abstract: We describe several protein sequence statistics designed to evaluate distinctive attributes of residue content and arrangement in primary structure. Considered are global compositional biases, local clustering of different residue types (e.g., charged residues, hydrophobic residues, Ser/Thr), long runs of charged or uncharged residues, periodic patterns, counts and distribution of homooligopeptides, and unusual spacings between particular residue types. The computer program SAPS (statistical analysis of protein sequences) calculates all the statistics for any individual protein sequence input and is available for the UNIX environment through electronic mail on request to V.B. (volker/genomic@stanford.edu).
authors.affiliation: Department of Mathematics, Stanford University, CA 94305-2125.
authors.forename: V
authors.initials: V
authors.lastname: Brendel
authors.forename: P
authors.initials: P
authors.lastname: Bucher
authors.forename: I R
authors.initials: IR
authors.lastname: Nourbakhsh
authors.forename: B E
authors.initials: BE
authors.lastname: Blaisdell
authors.forename: S
authors.initials: S
authors.lastname: Karlin
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D004330:Drosophila; D006801:Humans; D008969:Molecular Sequence Data; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D016271:Proto-Oncogene Proteins c-myc / Q000235:genetics*; D012689:Sequence Homology, Nucleic Acid
chemical_list: D011506:Proteins; D016271:Proto-Oncogene Proteins c-myc