Inferring DNA sequences from mechanical unzipping: an ideal-case study

Abstract

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.

Source as TeX file with ps figures

References in corpus (9)

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Cited by in corpus (12)

• Single-molecule experiments in biological physics: methods and applications
• Physics of base-pairing dynamics in DNA
• Statistical properties of metastable intermediates in DNA unzipping
• Stretching and twisting of the DNA duplexes in coarse grained dynamical models
• Inference of Markov models from trajectories via Large Deviations at Level 2.5 with applications to random walks in disordered media
• Inferring DNA sequences from mechanical unzipping data: the large-bandwidth case
• Reconstructing a Random Potential from its Random Walks
• Quenched limits for the fluctuations of transient random walks in random environment on Z
• Unzipping of two random heteropolymers: Ground state energy and finite size effects
• Dynamical modelling of molecular constructions and setups for DNA unzipping
• Sinai's walk: a statistical aspect
• Phase transition of disordered random networks on quasi-transitive graphs