Inferring DNA sequences from mechanical unzipping: an ideal-case study
- Resource Type
- Working Paper
- Authors
- Baldazzi, V.; Cocco, S.; Marinari, E.; Monasson, R.
- Source
- Subject
- Condensed Matter - Disordered Systems and Neural Networks
Condensed Matter - Statistical Mechanics
Quantitative Biology - Quantitative Methods
- Language
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.
Comment: Source as TeX file with ps figures