DeepMP: a deep learning tool to detect DNA base modifications on Nanopore sequencing data

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• dc.contributor.author Bonet, Jose
• dc.contributor.author Chen, Mandi
• dc.contributor.author Dabad, Marc
• dc.contributor.author Heath, Simon
• dc.contributor.author Gonzalez-Perez, Abel
• dc.contributor.author López Bigas, Núria
• dc.contributor.author Lagergren, Jens
• dc.date.accessioned 2021-12-21T11:07:24Z
• dc.date.available 2021-12-21T11:07:24Z
• dc.date.issued 2021
• dc.description.abstract Motivation: DNA Methylation plays a key role in a variety of biological processes. Recently, Nanopore long-read sequencing has enabled direct detection of these modifications. As a consequence, a range of computational methods have been developed to exploit Nanopore data for methylation detection. However, current approaches rely on a human-defined threshold to detect the methylation status of a genomic position and are not optimized to detect sites methylated at low frequency. Furthermore, most methods employ either the Nanopore signals or the basecalling errors as the model input and do not take advantage of their combination. Results: Here we present DeepMP, a convolutional neural network (CNN)-based model that takes information from Nanopore signals and basecalling errors to detect whether a given motif in a read is methylated or not. Besides, DeepMP introduces a threshold-free position modification calling model sensitive to sites methylated at low frequency across cells. We comprehensively benchmarked DeepMP against state-of-the-art methods on E. coli, human and pUC19 datasets. DeepMP outperforms current approaches at read-based and position-based methylation detection across sites methylated at different frequencies in the three datasets. Availability: DeepMP is implemented and freely available under MIT license at https://github.com/pepebonet/DeepMP. Supplementary information: Supplementary data are available at Bioinformatics online.
• dc.description.sponsorship This work was funded by ITN-CONTRA EU [H2020 MSCA-ITN-2017-766030 to J.B. and M.C.]
• dc.format.mimetype application/pdf
• dc.identifier.citation Bonet J, Chen M, Dabad M, Heath S, Gozalez-Perez A, Lopez-Bigas N et al. DeepMP: a deep learning tool to detect DNA base modifications on Nanopore sequencing data. Bioinformatics. 2021 Oct 28;btab745. DOI: 10.1093/bioinformatics/btab745
• dc.identifier.doi http://dx.doi.org/10.1093/bioinformatics/btab745
• dc.identifier.issn 1367-4803
• dc.identifier.uri http://hdl.handle.net/10230/51909
• dc.language.iso eng
• dc.publisher Oxford University Press
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/766030
• dc.rights © Josep Bonet et al. 2021. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/4.0/
• dc.subject.other Genètica
• dc.subject.other Genòmica
• dc.subject.other ADN -- Metilació
• dc.subject.other Informàtica -- Programaris
• dc.title DeepMP: a deep learning tool to detect DNA base modifications on Nanopore sequencing data
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion