Snapper: high-sensitive detection of methylation motifs based on Oxford Nanopore reads
Abstract Motivation The Oxford Nanopore technology has a great potential for the analysis of methylated motifs in genomes, including whole-genome methylome profiling. However, we found that there are no methylation motifs detection algorithms, which would be sensitive enough and return deterministic...
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Veröffentlicht in: | Bioinformatics (Oxford, England) England), 2023-11, Vol.39 (11) |
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Sprache: | eng |
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Zusammenfassung: | Abstract
Motivation
The Oxford Nanopore technology has a great potential for the analysis of methylated motifs in genomes, including whole-genome methylome profiling. However, we found that there are no methylation motifs detection algorithms, which would be sensitive enough and return deterministic results. Thus, the MEME suit does not extract all Helicobacter pylori methylation sites de novo even using the iterative approach implemented in the most up-to-date methylation analysis tool Nanodisco.
Results
We present Snapper, a new highly sensitive approach, to extract methylation motif sequences based on a greedy motif selection algorithm. Snapper does not require manual control during the enrichment process and has enrichment sensitivity higher than MEME coupled with Tombo or Nanodisco instruments that was demonstrated on H.pylori strain J99 studied earlier by the PacBio technology and on four external datasets representing different bacterial species. We used Snapper to characterize the total methylome of a new H.pylori strain A45. At least four methylation sites that have not been described for H.pylori earlier were revealed. We experimentally confirmed the presence of a new CCAG-specific methyltransferase and inferred a gene encoding a new CCAAK-specific methyltransferase.
Availability and implementation
Snapper is implemented using Python and is freely available as a pip package named “snapper-ont.” Also, Snapper and the demo dataset are available in Zenodo (10.5281/zenodo.10117651). |
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ISSN: | 1367-4803 1367-4811 |
DOI: | 10.1093/bioinformatics/btad702 |