title: An entropy-based technique for classifying bacterial chromosomes according to synonymous codon usage.
abstract: We present a framework based on information theoretic concepts and the Dirichlet distribution for classifying chromosomes based on the degree to which they use synonymous codons uniformly or preferentially, that is, whether or not codons that code for an amino acid appear with the same relative frequency. At its core is a measure of codon usage bias we call the Kullback-Leibler codon information bias (KL-CIB or CIB for short). Being defined in terms of conditional entropy makes KL-CIB an ideal and natural quantity for expressing a chromosome's degree of departure from uniform synonymous codon usage. Applying the approach to a large collection of annotated bacterial chromosomes reveals three distinct groups of bacteria.
authors.affiliation: UMI 2071 CNRS-UCHILE, Facultad de Ciencias Físicas y Matemáticas, Centro de Modelamiento Matemático, Universidad de Chile, Casilla 170, Correo 3, Santiago, Chile. ahart@dim.uchile.cl.
authors.forename: Andrew
authors.initials: A
authors.lastname: Hart
authors.affiliation: Departamento de Ingeniería Matemática, UMI 2071 CNRS-UCHILE, Facultad de Ciencias Físicas y Matemáticas, Centro de Modelamiento Matemático, Universidad de Chile, Casilla 170, Correo 3, Santiago, Chile.
authors.forename: Servet
authors.initials: S
authors.lastname: Martínez
mesh_terms: D000596:Amino Acids / Q000235:genetics; D001419:Bacteria / Q000145:classification / Q000235:genetics*; D002876:Chromosomes, Bacterial / Q000145:classification*; D003062:Codon / Q000235:genetics*; D019277:Entropy; D008962:Models, Theoretical
chemical_list: D000596:Amino Acids; D003062:Codon
keywords: Annotated bacteria; Conditional entropy; Dirichlet distribution; Entropy