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Published on: October 21, 2011
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Estimating DNA methylation potential energy landscapes from nanopore sequencing data
Jordi Abante1,2,3, Sandeep Kambhampati4,5, Andrew P Feinberg4,6,7
1Whitaker Biomedical Engineering Institute, Johns Hopkins University, Baltimore, MD, 21218, USA. jabante@stanford.edu.
Scientific Reports
|November 4, 2021
Summary
CpelNano is a new statistical method that accurately quantifies DNA methylation landscapes from noisy nanopore sequencing data. This tool improves the analysis of epigenetic modifications in human cells.
Area of Science:
- Genomics
- Epigenetics
- Bioinformatics
Background:
- Nanopore sequencing offers high-throughput analysis of epigenetic modifications across the genome.
- Significant noise in nanopore signals challenges accurate detection and downstream analysis of methylation data.
Purpose of the Study:
- To develop a robust statistical method, CpelNano, for quantifying and analyzing 5-methylcytosine (5mC) methylation landscapes using nanopore sequencing data.
- To address and mitigate the noise inherent in nanopore sequencing signals for improved epigenetic analysis.
Main Methods:
- Developed CpelNano, a statistical method employing a hidden Markov model (HMM) to account for nanopore noise.
- Modeled methylation states using an Ising probability distribution and current signals as observed states.
- Utilized the expectation-maximization (EM) algorithm for estimating methylation potential energy and permutation-based hypothesis testing for differential analysis.
Main Results:
- CpelNano accurately estimates DNA methylation potential energy landscapes.
- Demonstrated substantial improvement over existing methods in simulations and analysis of human cell line data (GM12878, MCF-10A, MDA-MB-231).
- Successfully applied to published nanopore sequencing data, validating its performance.
Conclusions:
- CpelNano provides a powerful and accurate tool for modeling and analyzing epigenetic landscapes from nanopore sequencing data.
- Significantly enhances the detection performance and reliability of epigenetic modification analysis, overcoming nanopore signal noise.
