  A new approach to estimate the Shannon entropy of a long-range correlated
sequence is proposed. The entropy is written as the sum of two terms
corresponding respectively to power-law (\emph{ordered}) and exponentially
(\emph{disordered}) distributed blocks (clusters). The approach is illustrated
on the 24 human chromosome sequences by taking the nucleotide composition as
the relevant information to be encoded/decoded. Interestingly, the nucleotide
composition of the \emph{ordered} clusters is found, on the average, comparable
to the one of the whole analyzed sequence, while that of the \emph{disordered}
clusters fluctuates. From the information theory standpoint, this means that
the power-law correlated clusters carry the same information of the whole
analysed sequence. Furthermore, the fluctuations of the nucleotide composition
of the disordered clusters are linked to relevant biological properties, such
as segmental duplications and gene density.
