TY - JOUR
T1 - Estimating DNA methylation potential energy landscapes from nanopore sequencing data
AU - Abante, Jordi
AU - Kambhampati, Sandeep
AU - Feinberg, Andrew P.
AU - Goutsias, John
N1 - Funding Information:
This work was supported by NSF Grant EFRI CEE 132452. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - High-throughput third-generation nanopore sequencing devices have enormous potential for simultaneously observing epigenetic modifications in human cells over large regions of the genome. However, signals generated by these devices are subject to considerable noise that can lead to unsatisfactory detection performance and hamper downstream analysis. Here we develop a statistical method, CpelNano, for the quantification and analysis of 5mC methylation landscapes using nanopore data. CpelNano takes into account nanopore noise by means of a hidden Markov model (HMM) in which the true but unknown (“hidden”) methylation state is modeled through an Ising probability distribution that is consistent with methylation means and pairwise correlations, whereas nanopore current signals constitute the observed state. It then estimates the associated methylation potential energy function by employing the expectation-maximization (EM) algorithm and performs differential methylation analysis via permutation-based hypothesis testing. Using simulations and analysis of published data obtained from three human cell lines (GM12878, MCF-10A, and MDA-MB-231), we show that CpelNano can faithfully estimate DNA methylation potential energy landscapes, substantially improving current methods and leading to a powerful tool for the modeling and analysis of epigenetic landscapes using nanopore sequencing data.
AB - High-throughput third-generation nanopore sequencing devices have enormous potential for simultaneously observing epigenetic modifications in human cells over large regions of the genome. However, signals generated by these devices are subject to considerable noise that can lead to unsatisfactory detection performance and hamper downstream analysis. Here we develop a statistical method, CpelNano, for the quantification and analysis of 5mC methylation landscapes using nanopore data. CpelNano takes into account nanopore noise by means of a hidden Markov model (HMM) in which the true but unknown (“hidden”) methylation state is modeled through an Ising probability distribution that is consistent with methylation means and pairwise correlations, whereas nanopore current signals constitute the observed state. It then estimates the associated methylation potential energy function by employing the expectation-maximization (EM) algorithm and performs differential methylation analysis via permutation-based hypothesis testing. Using simulations and analysis of published data obtained from three human cell lines (GM12878, MCF-10A, and MDA-MB-231), we show that CpelNano can faithfully estimate DNA methylation potential energy landscapes, substantially improving current methods and leading to a powerful tool for the modeling and analysis of epigenetic landscapes using nanopore sequencing data.
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U2 - 10.1038/s41598-021-00781-x
DO - 10.1038/s41598-021-00781-x
M3 - Article
C2 - 34732768
AN - SCOPUS:85118532007
VL - 11
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 21619
ER -