Abstract
Hidden Markov models (HMMs) are effective tools to detect series of statistically homogeneous structures, but they are not well suited to analyse complex structures. For example, the duration of stay in a state of a HMM must follow a geometric law. Numerous other methodological difficulties are encountered when using HMMs to segregate genes from transposons or retroviruses, or to determine the isochore classes of genes. The aim of this paper is to analyse these methodological difficulties, and to suggest new tools for the exploration of genome data. We show that HMMs can be used to analyse complex gene structures with bell-shaped length distribution by using convolution of geometric distributions. Thus, we have introduced macros-states to model the distributions of the lengths of the regions. Our study shows that simple HMM could be used to model the isochore organisation of the mouse genome. This potential use of markovian models to help in data exploration has been underestimated until now.
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