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DNA comprises molecular information stored in genetic and epigenetic bases, both of which are vital to our understanding of biology. Most DNA sequencing approaches address either genetics or epigenetics and thus capture incomplete information. Methods widely used to detect epigenetic DNA bases fail to capture common C-to-T mutations or distinguish 5-methylcytosine from 5-hydroxymethylcytosine. We present a single base-resolution sequencing methodology that sequences complete genetics and the two most common cytosine modifications in a single workflow. DNA is copied and bases are enzymatically converted. Coupled decoding of bases across the original and copy strand provides a phased digital readout. Methods are demonstrated on human genomic DNA and cell-free DNA from a blood sample of a patient with cancer. The approach is accurate, requires low DNA input and has a simple workflow and analysis pipeline. Simultaneous, phased reading of genetic and epigenetic bases provides a more complete picture of the information stored in genomes and has applications throughout biomedicine.

DNA comprises molecular information stored via genetic bases (G, C, T, A) and also epigenetic bases, principally 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC). Both genetic and epigenetic information are vital to our understanding of biology and disease states. Most DNA sequencing approaches address either genetics or epigenetics and thus capture incomplete information. Methods widely used to detect epigenetic DNA bases typically fail to capture common C-to-T mutations or distinguish 5mC from 5hmC. Here, we present a single-base-resolution sequencing methodology that will simultaneously sequence complete genetics and complete epigenetics in a single workflow. The approach is non-destructive to DNA and provides a digital readout of bases, which we exemplify by simultaneous sequencing of G, C, T, A, 5mC and 5hmC; 6-Letter sequencing. We demonstrate sequencing of human genomic DNA and also cell-free DNA taken from a blood sample of a cancer patient. The approach is accurate, requires low DNA input and has a simple workflow and analysis pipeline. We envisage it will be versatile across many applications in life sciences.