Statistical inference for nanopore sequencing with a biased random walk model.
Ontology highlight
ABSTRACT:
SUBMITTER: Emmett KJ
PROVIDER: S-EPMC4407257 | biostudies-literature | 2015 Apr
REPOSITORIES: biostudies-literature
Publications
Statistical inference for nanopore sequencing with a biased random walk model.
Emmett Kevin J KJ Rosenstein Jacob K JK van de Meent Jan-Willem JW Shepard Ken L KL Wiggins Chris H CH
Biophysical journal 20150401 8
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 bou ...[more]
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