Estimating DNA methylation potential energy landscapes from nanopore sequencing data

Abstract 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 unsatisf...

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Autores principales:	Jordi Abante, Sandeep Kambhampati, Andrew P. Feinberg, John Goutsias
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1dd7b0607dc5416e8c94fe3f58dcf75f
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spelling	oai:doaj.org-article:1dd7b0607dc5416e8c94fe3f58dcf75f2021-11-08T10:53:52ZEstimating DNA methylation potential energy landscapes from nanopore sequencing data10.1038/s41598-021-00781-x2045-2322https://doaj.org/article/1dd7b0607dc5416e8c94fe3f58dcf75f2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-00781-xhttps://doaj.org/toc/2045-2322Abstract 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.Jordi AbanteSandeep KambhampatiAndrew P. FeinbergJohn GoutsiasNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jordi Abante Sandeep Kambhampati Andrew P. Feinberg John Goutsias Estimating DNA methylation potential energy landscapes from nanopore sequencing data
description	Abstract 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.
format	article
author	Jordi Abante Sandeep Kambhampati Andrew P. Feinberg John Goutsias
author_facet	Jordi Abante Sandeep Kambhampati Andrew P. Feinberg John Goutsias
author_sort	Jordi Abante
title	Estimating DNA methylation potential energy landscapes from nanopore sequencing data
title_short	Estimating DNA methylation potential energy landscapes from nanopore sequencing data
title_full	Estimating DNA methylation potential energy landscapes from nanopore sequencing data
title_fullStr	Estimating DNA methylation potential energy landscapes from nanopore sequencing data
title_full_unstemmed	Estimating DNA methylation potential energy landscapes from nanopore sequencing data
title_sort	estimating dna methylation potential energy landscapes from nanopore sequencing data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1dd7b0607dc5416e8c94fe3f58dcf75f
work_keys_str_mv	AT jordiabante estimatingdnamethylationpotentialenergylandscapesfromnanoporesequencingdata AT sandeepkambhampati estimatingdnamethylationpotentialenergylandscapesfromnanoporesequencingdata AT andrewpfeinberg estimatingdnamethylationpotentialenergylandscapesfromnanoporesequencingdata AT johngoutsias estimatingdnamethylationpotentialenergylandscapesfromnanoporesequencingdata
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Estimating DNA methylation potential energy landscapes from nanopore sequencing data

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