Representing Information on DNA using Patterns Induced by Enzymatic Labeling

TL;DR

This paper introduces a formal framework for representing information on DNA through enzymatic labeling patterns, advancing DNA data storage techniques with optimized encoding strategies.

Contribution

It presents a novel modeling approach for DNA labeling in data storage, including bounds on code size and an optimal encoder-decoder pair.

Findings

Upper bounds on code sizes for DNA labeling channels

Development of an optimal encoder-decoder pair

Analysis of fixed-length label constraints

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 maximal size of the corresponding codes are given. The study includes the development of an efficient encoder-decoder pair that is proven optimal in terms of maximum code size under specific conditions.