Discussion of "A Bayesian approach to DNA sequence segmentation".

This article discusses the results in Boys and Henderson (2004, Biometrics 60, 573-581) in which the authors propose a new approach to the classification of genomic DNA into a number of hidden Markov states with a variable order of dependency, potentially allowing for the high-throughput detection of structure within genomic DNA. This article is likely to be an important point of departure for further modeling of this type. We question whether the genome of the bacteriophage lambda is the most appropriate example with which to demonstrate the method's effectiveness, whether it can be expected that the method will carry over to genomes where there is only one direction of transcription and no operon structure, and suggest a graphical display that seems to offer insight into the results.

An efficient Z-score algorithm for assessing sequence alignments.

We describe an alternative method for scoring of the pairwise alignment of two biological sequences. Designed to overcome the bias due to the composition of the alignment, it measures the distance (in standard deviations) between the given alignment and the mean value of all other alignments that can be obtained by a permutation of either sequence. We demonstrate that the standard deviation can be calculated efficiently.

Modelling and bioinformatics studies of the human Kappa-class glutathione transferase predict a novel third glutathione transferase family with similarity to prokaryotic 2-hydroxychromene-2-carboxylate isomerases.

The Kappa class of GSTs (glutathione transferases) comprises soluble enzymes originally isolated from the mitochondrial matrix of rats. We have characterized a Kappa class cDNA from human breast. The cDNA is derived from a single gene comprising eight exons and seven introns located on chromosome 7q34-35.