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Biophys J. 2015 Apr 21;108(8):1852-5. doi: 10.1016/j.bpj.2015.03.013.

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

Author information

1
Department of Physics, Columbia University, New York, New York. Electronic address: kje@phys.columbia.edu.
2
School of Engineering, Brown University, Providence, Rhode Island.
3
Department of Statistics, Columbia University, New York, New York.
4
Department of Electrical Engineering, Columbia University, New York, New York.
5
Department of Applied Physics and Applied Math, Columbia University, New York, New York.

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.

PMID:
25902425
PMCID:
PMC4407257
DOI:
10.1016/j.bpj.2015.03.013
[Indexed for MEDLINE]
Free PMC Article

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