Snapper

Snapper detects methylation motifs from Oxford Nanopore sequencing data to enable high-sensitivity, deterministic identification of methylation motifs and associated methyltransferases in microbial genomes.

Key Features:

• High Sensitivity: Employs approaches that provide sensitivity beyond methods that combine MEME suite with Tombo or Nanodisco for methylation motif detection.
• Greedy Motif Selection Algorithm: Uses a greedy motif selection algorithm to enhance motif discovery sensitivity during enrichment.
• Deterministic Results: Produces deterministic enrichment and motif-calling outcomes without requiring manual intervention in the enrichment process.
• Oxford Nanopore Data Processing: Operates on genomic data derived from Oxford Nanopore sequencing for methylome analysis.
• Demonstrated on Bacterial Genomes: Applied to multiple bacterial datasets, including Helicobacter pylori strain J99 and strain A45.
• Novel Methylation Site Discovery: Identified at least four previously unreported methylation sites in H. pylori strain A45.
• Experimental Validation of Enzymes: Supported experimental identification of a CCAG-specific methyltransferase and inference of a gene encoding a CCAAK-specific methyltransferase in H. pylori.
• Scalable Analysis: Designed to process large genomic datasets for comprehensive methylome characterization.

Scientific Applications:

• Whole-genome methylome profiling: Enables genome-wide detection of methylation motifs from Oxford Nanopore sequencing data.
• Bacterial epigenetics: Facilitates discovery and characterization of methylation motifs and methyltransferases in bacteria, exemplified by Helicobacter pylori strains J99 and A45.
• Novel motif and enzyme discovery: Supports identification of previously unreported methylation sites and associated methyltransferases.
• Comparative methylome analysis: Applicable to comparative studies of methylation patterns across bacterial strains and species.

Methodology:

Implemented in Python, Snapper analyzes Oxford Nanopore sequencing data using a greedy motif selection algorithm to perform deterministic motif enrichment and high-sensitivity methylation motif detection.