  We introduce and test a method to predict the sequence of DNA molecules from
in silico unzipping experiments. The method is based on Bayesian inference and
on the Viterbi decoding algorithm. The probability of misprediction decreases
exponentially with the number of unzippings, with a decay rate depending on the
applied force and the sequence content.

Disordered Systems and Neural Networks;Statistical Mechanics;Quantitative Methods