The isochore structure of a genome is observable by variation in the G+C 
(guanine and cytosine) content within and between the chromosomes. Describing 
the isochore structure of vertebrate genomes is a challenging task, and many 
computational methods have been developed and applied to it. Here we apply a 
well-known least-squares optimal segmentation algorithm to isochore discovery. 
The algorithm finds the best division of the sequence into k pieces, such that 
the segments are internally as homogeneous as possible. We show how this simple 
segmentation method can be applied to isochore discovery using as input the G+C 
content of sliding windows on the sequence. To evaluate the performance of this 
segmentation technique on isochore detection, we present results from segmenting 
previously studied isochore regions of the human genome. Detailed results on the 
MHC locus, on parts of chromosomes 21 and 22, and on a 100 Mb region from 
chromosome 1 are similar to previously suggested isochore structures. We also 
give results on segmenting all 22 autosomal human chromosomes. An advantage of 
this technique is that oversegmentation of G+C rich regions can generally be 
avoided. This is because the technique concentrates on greater global, instead 
of smaller local, differences in the sequence composition. The effect is further 
emphasized by a log-transformation of the data that lowers the high variance 
that is observed in G+C rich regions. We conclude that the least-squares optimal 
segmentation method is computationally efficient and yields results close to 
previous biologically motivated isochore structures.