Analysis of nanopore data using hidden Markov models #MMPMID25649617
Schreiber J; Karplus K
Bioinformatics 2015[Jun]; 31 (12): 1897-903 PMID25649617show 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.Contact:karplus@soe.u csc.edu or jmschreiber91@gmail.comSupplementary information:Supplementary data are available from Bioinformatics online.
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Bioinformatics 2015 ; 31 (12): 1897-903
