  We investigate on a possible way to connect the presence of Low-Complexity
Sequences (LCS) in DNA genomes and the nonstationary properties of base
correlations. Under the hypothesis that these variations signal a change in the
DNA function, we use a new technique, called Non-Stationarity Entropic Index
(NSEI) method, and we prove that this technique is an efficient way to detect
functional changes with respect to a random baseline. The remarkable aspect is
that NSEI does not imply any training data or fitting parameter, the only
arbitrarity being the choice of a marker in the sequence. We make this choice
on the basis of biological information about LCS distributions in genomes. We
show that there exists a correlation between changing the amount in LCS and the
ratio of long- to short-range correlation.
