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

TL;DR

This paper presents a Bayesian inference-based method using Viterbi decoding to accurately predict DNA sequences from simulated unzipping experiments, with error rates decreasing exponentially as more data is collected.

Contribution

It introduces a novel computational approach for DNA sequence inference from mechanical unzipping data, demonstrating its effectiveness through idealized simulations.

Findings

Error probability decreases exponentially with more unzipping data

Decay rate of misprediction depends on applied force and sequence content

Method achieves high accuracy in idealized conditions

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.