Statistical inference for nanopore sequencing with a biased random walk model.

Nanopore sequencing promises long read-lengths and single-molecule resolution, but the stochastic motion of the DNA molecule inside the pore is, as of this writing, a barrier to high accuracy reads. We develop a method of statistical inference that explicitly accounts for this error, and demonstrate that high accuracy (>99%) sequence inference is feasible even under highly diffusive motion by using a hidden Markov model to jointly analyze multiple stochastic reads. Using this model, we place bounds on achievable inference accuracy under a range of experimental parameters.

Differentiation of short, single-stranded DNA homopolymers in solid-state nanopores.

In the last two decades, new techniques that monitor ionic current modulations as single molecules pass through a nanoscale pore have enabled numerous single-molecule studies. While biological nanopores have recently shown the ability to resolve single nucleotides within individual DNA molecules, similar developments with solid-state nanopores have lagged, due to challenges both in fabricating stable nanopores of similar dimensions as biological nanopores and in achieving sufficiently low-noise and high-bandwidth recordings. Here we show that small silicon nitride nanopores (0.