  In this paper, we extend a previously developed recursive entropic
segmentation scheme for applications to biological sequences. Instead of
Bernoulli chains, we model the statistically stationary segments in a
biological sequence as Markov chains, and define a generalized Jensen-Shannon
divergence for distinguishing between two Markov chains. We then undertake a
mean-field analysis, based on which we identify pitfalls associated with the
recursive Jensen-Shannon segmentation scheme. Following this, we explain the
need for segmentation optimization, and describe two local optimization schemes
for improving the positions of domain walls discovered at each recursion stage.
We also develop a new termination criterion for recursive Jensen-Shannon
segmentation based on the strength of statistical fluctuations up to a minimum
statistically reliable segment length, avoiding the need for unrealistic null
and alternative segment models of the target sequence. Finally, we compare the
extended scheme against the original scheme by recursively segmenting the
Escherichia coli K-12 MG1655 genome.
