Single-molecule real-time (SMRT) DNA sequencing allows the systematic detection of chemical modifications such as methylation but has not previously been applied on a genome-wide scale. We used this approach to detect 49,311 putative 6-methyladenine (m6A) residues and 1,407 putative 5-methylcytosine (m5C) residues in the genome of a pathogenic Escherichia coli strain. We obtained strand-specific information for methylation sites and a quantitative assessment of the frequency of methylation at each modified position. We deduced the sequence motifs recognized by the methyltransferase enzymes present in this strain without prior knowledge of their specificity. Furthermore, we found that deletion of a phage-encoded methyltransferase-endonuclease (restriction-modification; RM) system induced global transcriptional changes and led to gene amplification, suggesting that the role of RM systems extends beyond protecting host genomes from foreign DNA.

Current generation DNA sequencing instruments are moving closer to seamlessly sequencing genomes of entire populations as a routine part of scientific investigation. However, while significant inroads have been made identifying small nucleotide variation and structural variations in DNA that impact phenotypes of interest, progress has not been as dramatic regarding epigenetic changes and base-level damage to DNA, largely due to technological limitations in assaying all known and unknown types of modifications at genome scale. Recently, single-molecule real time (SMRT) sequencing has been reported to identify kinetic variation (KV) events that have been demonstrated to reflect epigenetic changes of every known type, providing a path forward for detecting base modifications as a routine part of sequencing. However, to date no statistical framework has been proposed to enhance the power to detect these events while also controlling for false-positive events. By modeling enzyme kinetics in the neighborhood of an arbitrary location in a genomic region of interest as a conditional random field, we provide a statistical framework for incorporating kinetic information at a test position of interest as well as at neighboring sites that help enhance the power to detect KV events. The performance of this and related models is explored, with the best-performing model applied to plasmid DNA isolated from Escherichia coli and mitochondrial DNA isolated from human brain tissue. We highlight widespread kinetic variation events, some of which strongly associate with known modification events, while others represent putative chemically modified sites of unknown types. [Supplemental material is available for this article.] DNA sequencing carried out using current first and second (or next) generation sequencing (NGS) instruments has achieved significant success in providing DNA sequences in which the A's, G's, C's, and T's comprising any given DNA template of interest are very consistently called at accuracies that exceed 99%, enabling an explosion of whole-genome sequencing applications that promise to transform our understanding of living systems and usher in the era of personalized genomics (Shendure and Ji 2008; Wheeler et al.

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