title: 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.
authors.affiliation: Department of Physics, Columbia University, New York, New York. Electronic address: kje@phys.columbia.edu.
authors.forename: Kevin J
authors.initials: KJ
authors.lastname: Emmett
authors.affiliation: School of Engineering, Brown University, Providence, Rhode Island.
authors.forename: Jacob K
authors.initials: JK
authors.lastname: Rosenstein
authors.affiliation: Department of Statistics, Columbia University, New York, New York.
authors.forename: Jan-Willem
authors.initials: JW
authors.lastname: van de Meent
authors.affiliation: Department of Electrical Engineering, Columbia University, New York, New York.
authors.forename: Ken L
authors.initials: KL
authors.lastname: Shepard
authors.affiliation: Department of Applied Physics and Applied Math, Columbia University, New York, New York.
authors.forename: Chris H
authors.initials: CH
authors.lastname: Wiggins
mesh_terms: D004247:DNA / Q000737:chemistry*; D015233:Models, Statistical; D058608:Nanopores; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D004247:DNA