Abstract
DNA chemical modifications regulate genomic function. We present a framework for mapping cytosine and adenosine methylation with the Oxford Nanopore Technologies MinION using this nanopore sequencer's ionic current signal. We map three cytosine variants and two adenine variants. The results show that our model is sensitive enough to detect changes in genomic DNA methylation levels as a function of growth phase in Escherichia coli.
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Acknowledgements
Research reported in this publication was supported by the National Human Genome Research Institute of the US National Institutes of Health under award numbers HG006321 (M.A.), HG007827 (M.A.) and 5U54HG007990 (B.P.).
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Contributions
B.P. conceived of the experiments. B.P. and M.A. directed the research. A.C.R. implemented the models and performed analysis. M.J. and H.E.O. performed the sequencing experiments and performed sequence data analysis. J.M.E. implemented the HDP model and Gibbs sampler. A.M.-B. performed initial experiments. All authors contributed to writing the manuscript.
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M.A. is a consultant to Oxford Nanopore Technologies.
Integrated supplementary information
Supplementary Figure 1 Overview of models
A. Architecture of hidden Markov model used in this study. The match state ‘M’ (square) emits an event/k-mer pair and proceeds along the reference and the event sequence, Insert-Y ‘Iy’ (diamond) emits a pair and proceeds along the event sequence but stays in place with respect to the reference, and Insert-X ‘Ix’ (circle) proceeds along the reference but does not emit a pair and stays in place with respect to the event sequence. B. Variable-order HMM meta-structure over an example reference sequence containing ambiguous methylation variants. Each C* in the reference represents a potentially methylated cytosine. The structure expands around the C* to accommodate all possible methylation states (in this case, C, 5-mC, and 5-hmC). Each cell contains the three states shown in A, and transitions span between cells. The transitions are restricted so that methylation states are labeled consistently within a path. The match states are drawn with 4-mers for simplicity, but the model is implemented with 5-mers and 6-mers. Two-level (C) and three-level (D) hierarchical Dirichlet process shown in graphical form. Circles represent random variables. The base distribution ‘H’ is a normal inverse-gamma distribution for both models. The Dirichlet processes ‘G0’, ‘Gσn’, and ‘Gσni’ are parameterized by their parent distribution and shared concentration parameters ‘γB’, γM’, and γL’. The factors ‘θji’ specify the parameters of the normal distribution mixture component that generates observation ‘xji’.
Supplementary Figure 3 Probability distributions for three representative 6-mers by multiple methods.
The first row shows the kernel density estimate (KDE) based on the preliminary alignments described in the text. The middle row shows maximum likelihood estimated (MLE) normal distribution probability density functions. The bottom row shows probability density functions from the ‘Multiset’ hierarchical Dirichlet process (HDP). All data shown are from template reads.
Supplementary Figure 4 Correlation between ungapped alignment score (see Methods) and per-read accuracy for 500 randomly sampled complement reads.
Per-read accuracy is the percent of correctly called methylation variants for 6-mA in GATC motifs (left) and 5-mC in CC(A/T)GG motifs (right). The pUC19 sequence contains 30 potentially methylated adenine residues in 15 GATC motifs and 10 potentially methylated cytosine residues in 5 CC(A/T)GG motifs.
Supplementary Figure 5 Legal transitions in variable-order HMM.
Transitions between cells in the dynamic programming matrix are only allowed between k-mers where the last k-1 bases of the first k-mer (AGEOAT) match the first k-1 bases of the next (GEOATA).
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Supplementary Text and Figures
Supplementary Figures 1–5, Supplementary Tables 1–3, Supplementary Note and Supplementary Discussion (PDF 1207 kb)
Supplementary Software
SignalAlign software (ZIP 63275 kb)
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Rand, A., Jain, M., Eizenga, J. et al. Mapping DNA methylation with high-throughput nanopore sequencing. Nat Methods 14, 411–413 (2017). https://doi.org/10.1038/nmeth.4189
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DOI: https://doi.org/10.1038/nmeth.4189
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