  The complementary strands of DNA molecules can be separated when stretched
apart by a force; the unzipping signal is correlated to the base content of the
sequence but is affected by thermal and instrumental noise. We consider here
the ideal case where opening events are known to a very good time resolution
(very large bandwidth), and study how the sequence can be reconstructed from
the unzipping data. Our approach relies on the use of statistical Bayesian
inference and of Viterbi decoding algorithm. Performances are studied
numerically on Monte Carlo generated data, and analytically. We show how
multiple unzippings of the same molecule may be exploited to improve the
quality of the prediction, and calculate analytically the number of required
unzippings as a function of the bandwidth, the sequence content, the elasticity
parameters of the unzipped strands.
