A bioinformatic pipeline for analysis of M.EcoGII methylation footprint PacBio long-read sequence data
Recent studies have combined DNA methyltransferase footprinting of genomic DNA in nuclei with long-read sequencing, resulting in detailed chromatin maps for multi-kilobase stretches of genomic DNA from one cell. Nucleosome footprints and nucleosome-depleted regions can be identified, yielding unprecedented information concerning their degree of correlation within the same cell. The enzyme of choice is M.EcoGII, which methylates adenines in any sequence context, potentially resulting in very high resolution. However, in practice, the methylation efficiency (defined as the fraction of each genomic adenine that is methylated), is quite low, resulting in false footprints predicted by random unmethylated gaps between methylated adenines. We report PacBio long-read sequence data for budding yeast nuclei treated with M.EcoGII. We present a bioinformatic pipeline which accounts for methylation efficiency, as well as an observed bias against methylation as a function of increasing AT content. It also accounts for our observation that some methylation occurs within nucleosomes, breaking up their footprints. Comparison of long reads for each gene indicates the extent of chromatin heterogeneity within the cell population. Although the population average is consistent with that derived using other techniques, we observe a wide range of heterogeneity in nucleosome positions at the single-molecule level. Overall design: PacBio long-read sequences. Two biological replicate experiments comprised of 3 samples each: budding yeast nuclei treated with M.EcoGII, mock-treated nuclei (negative control) and genomic DNA treated with M.EcoGII (positive control) (6 samples). We also performed a test experiment for m6A identification by PacBio using pUC19 plasmid linearised with SmaI and treated in vitro with M.EcoGII methylase, or dam methylase, or untreated (6 samples). Total of 12 samples.