Statistical Inference for Nanopore Sequencing with a Biased Random Walk Model

Abstract

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.

Wavelet Denoising of High-Bandwidth Nanopore and Ion-Channel Signals

“…Bessel filters are generally employed for this purpose because of their uniform group delay characteristics. 31 In addition to Bessel filters, other denoising approaches that rely on hidden Markov models (HMMs) have been applied to both nanopore 32,33 and ionchannel recordings. 34−36 However, these techniques require assumptions to be made about the underlying model and are, in general, more computationally expensive than the denoising technique explored here.…”

“…Bessel filters are generally employed for this purpose because of their uniform group delay characteristics . In addition to Bessel filters, other denoising approaches that rely on hidden Markov models (HMMs) have been applied to both nanopore , and ion-channel recordings. − However, these techniques require assumptions to be made about the underlying model and are, in general, more computationally expensive than the denoising technique explored here. This computational complexity further increases in the context of signals corrupted by f 2 noise, where “meta-states” may need to be introduced to deal with the correlation between noise in adjacent data points …”