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2015 ; 31
(12
): 1897-903
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Analysis of nanopore data using hidden Markov models
#MMPMID25649617
Schreiber J
; Karplus K
Bioinformatics
2015[Jun]; 31
(12
): 1897-903
PMID25649617
show ga
MOTIVATION: Nanopore-based sequencing techniques can reconstruct properties of
biosequences by analyzing the sequence-dependent ionic current steps produced as
biomolecules pass through a pore. Typically this involves alignment of new data
to a reference, where both reference construction and alignment have been
performed by hand. RESULTS: We propose an automated method for aligning nanopore
data to a reference through the use of hidden Markov models. Several features
that arise from prior processing steps and from the class of enzyme used can be
simply incorporated into the model. Previously, the M2MspA nanopore was shown to
be sensitive enough to distinguish between cytosine, methylcytosine and
hydroxymethylcytosine. We validated our automated methodology on a subset of that
data by automatically calculating an error rate for the distinction between the
three cytosine variants and show that the automated methodology produces a 2-3%
error rate, lower than the 10% error rate from previous manual segmentation and
alignment. AVAILABILITY AND IMPLEMENTATION: The data, output, scripts and
tutorials replicating the analysis are available at
https://github.com/UCSCNanopore/Data/tree/master/Automation.
free
free
free
