PT  - JOURNAL ARTICLE
AU  - Jordi Abante
AU  - Sandeep Kambhampati
AU  - Andrew P. Feinberg
AU  - John Goutsias
TI  - Estimating DNA methylation potential energy landscapes from nanopore sequencing data
AID  - 10.1101/2021.02.22.431480
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.02.22.431480
4099  - http://biorxiv.org/content/early/2021/02/24/2021.02.22.431480.short
4100  - http://biorxiv.org/content/early/2021/02/24/2021.02.22.431480.full
AB  - High-throughput third-generation sequencing devices, such as the Oxford Nanopore Technologies (ONT) MinION sequencer, can generate long reads that span thousands of bases. This new technology opens the possibility of considering a wide range of epigenetic modifications and provides the capability of interrogating previously inaccessible regions of the genome, such as highly repetitive regions, as well as of performing comprehensive allele-specific methylation analysis, among other applications. It is well-known, however, that detection of DNA methylation from nanopore data results in a substantially reduced per-read accuracy when comparing to WGBS, due to noise introduced by the sequencer and its underlying chemistry. It is therefore imperative that methods are developed for the reliable modeling and analysis of the DNA methylation landscape using nanopore data. Here we introduce such method that takes into account the presence of noise introduced by the ONT sequencer and, by using simulations, we provide evidence of its potential. The proposed approach establishes a solid foundation for the development of a comprehensive framework for the statistical analysis of DNA methylation, and possibly of other epigenetic marks, using third-generation sequencing.Competing Interest StatementThe authors have declared no competing interest.