NGS data related to Sogl et al. "Systematic analysis of specificities and flanking sequence preferences of bacterial DNA-(cytosine C5)-methyltransferases reveals mechanisms of enzyme- and sequence-specific DNA readout"

Samples were eluted with RNase free H2O. Library preparation was performed with two PCRs using variable primer pairs to introduce sample specific barcodes and indices for sample distinction and the sequencing reactions. Bioinformatic analysis of the NGS data was conducted as described (Dukatz, et al. 2020; Dukatz, et al. 2022).

For determination of the methylation rates of all 256 NNCGNN sequences by one enzyme, methylation reactions of individual substrates were assumed to be independent and reaction velocities are of first order with respect to the substrate concentrations. The results of the individual reactions with different enzyme concentrations and incubation times were fitted to monoexponential reaction progress curves using variable virtual time values.

Fitting was conducted with MatLab as described except that convergence was validated by serial fitting (Adam, et al. 2022). References Adam S, Bräcker J, Klingel V, Osteresch B, Radde NE, Brockmeyer J, Bashtrykov P, Jeltsch A. Flanking sequences influence the activity of TET1 and TET2 methylcytosine dioxygenases and affect genomic 5hmC patterns. Communications Biology 5, 92 (2022) Dukatz M, Dittrich M, Stahl E, Adam S, de Mendoza A, Bashtrykov P, Jeltsch A. DNA methyltransferase DNMT3A forms interaction networks with the CpG site and flanking sequence elements for efficient methylation.

J. Biol. Chem. 298(10), 102462 (2022) Dukatz M, Adam S, Biswal M, Song J, Bashtrykov P, Jeltsch A. Complex DNA sequence readout mechanisms of the DNMT3B DNA methyltransferase. Nucleic Acids Res 48, 11495-11509 (2020)