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arXiv · 2610.24171

Representing Information on DNA using Patterns Induced by Enzymatic Labeling

Daniella Bar-Lev·Eitan Yaakobi·Zohar Yakhini·Tuvi Etzion
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Abstract

Enzymatic DNA labeling is a powerful tool with applications in biochemistry, molecular biology, biotechnology, medical science, and genomic research. This paper contributes to the evolving field of DNA-based data storage by presenting a formal framework for modeling DNA labeling in strings, specifically tailored for data storage purposes. Our approach involves a known DNA molecule as a template for labeling, employing patterns induced by a set of designed labels to represent information. One hypothetical implementation can use CRISPR-Cas9 and gRNA reagents for labeling. Various aspects of the general labeling channel, including fixed-length labels, are explored, and upper bounds on the maximum size of the corresponding codes are given. The study includes the development of an efficient encoder-decoder pair that is proven to be optimal in terms of maximum code size under specific conditions. The codewords for this framework are codesets, i.e., each codeword is a collection of a few labels. Of special interest are codes whose codesets contain fixed-length labels. Their construction and analysis are tied to de Bruijn sequences and constrained coding.

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Daniella Bar-Lev, Eitan Yaakobi, Zohar Yakhini, Tuvi Etzion. 2026-10-16. Representing Information on DNA using Patterns Induced by Enzymatic Labeling. https://arxiv.org/abs/2610.24171

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