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While the archival digital memory industry approaches its physical limits, the demand is significantly increasing, therefore alternatives emerge. Recent efforts have demonstrated DNA’s enormous potential as a digital storage medium with superior information durability, capacity, and energy consumption. However, the majority of the proposed systems require on-demand de-novo DNA synthesis techniques that produce a large amount of toxic waste and therefore are not industrially scalable and environmentally friendly. Inspired by the architecture of semiconductor memory devices and recent developments in gene editing, we created a molecular digital data storage system called “DNA Mutational Overwriting Storage” (DMOS) that stores information by leveraging combinatorial, addressable, orthogonal, and independent in vitro CRISPR base-editing reactions to write data on a blank pool of greenly synthesized DNA tapes. As a proof of concept, this work illustrates writing and accurately reading of both a bitmap representation of our school’s logo and the title of this study on the DNA tapes.

 

Deoxyribonucleic acid (DNA) is emerging as an alternative archival memory technology. Recent advancements in DNA synthesis and sequencing have both increased the capacity and decreased the cost of storing information in de novo synthesized DNA pools. In this survey, we review methods for translating digital data to and/or from DNA molecules. An emphasis is placed on methods which have been validated by storing and retrieving real-world data via in-vitro experiments.

 

Structural DNA nanotechnology is a powerful technique for bottom-up self-assembly of nanoscale structures. Potential applications are vast and only limited by the researchers' imagination. For large and complex structures, the manual or semi-automatic designing process is time-consuming and requires a detailed inspection of the model, leading to user error. We introduce MENDEL, a software library that allows the automatic, extensive, and parametric DNA nanostructures design in this work. MENDEL contains a set of commands that automate the designing process, allow the abstraction of turning sites, compute staples, and parametrize scaling and repetitive features; thus, reducing user error, design complications, and time-to-complete. Running MENDEL through Blender renders a 3D representation of the model. Also, for community convenience, MENDEL generates caDNAno/CanDo compatible files. MENDEL is available as open-source software at https://github.com/SBMI-LAB/MENDEL. 

Designing complex DNA nanostructures is a complicated process that requires efficient software to calculate and populate structural details. Most of the published software require manual manipulation and careful inspection of the models that increase the time cost and user error and decrease the flexibility of designing process. We created a python library that we coined MENDEL as a flexible and robust solution for automatic design of complex DNA nanostructures. MENDEL receives a set of sequential commands and creates the structures by following logical steps. Each step instructs the growth of the DNA nanostructure either by adding new nucleotides or repeating sections of arbitrary size and shape. User is able to monitor the design progress by executing the commands in Blender software’s scripting mode. Figure 1 shows an example of using MENDEL library to design a triple-layered origami that represents the word “MENDEL.” MENDEL generates the geometry preview, which helps to understand the design details. Moreover, for convenience, the exported file is compatible with caDNAno. Figure 2 shows the exported model when opened in caDNAno, and Figure 3 shows the modeling results obtained from CanDo for different number of layers. Future work include improving nucleotide twist and rise calculations, supporting honeycomb designs, detecting overlaps, inserting and skipping nucleotides, and generating molecular file formats such as Protein Data Bank (PDB). 

DNA is a compelling alternative to non-volatile information storage technologies due to its information density, stability, and energy efficiency. Previous studies have used artificially synthesized DNA to store data and automated next-generation sequencing to read it back. Here, we report digital Nucleic Acid Memory (dNAM) for applications that require a limited amount of data to have high information density, redundancy, and copy number. In dNAM, data is encoded by selecting combinations of single-stranded DNA with (1) or without (0) docking-site domains. When self-assembled with scaffold DNA, staple strands form DNA origami breadboards. Information encoded into the breadboards is read by monitoring the binding of fluorescent imager probes using DNA-PAINT super-resolution microscopy. To enhance data retention, a multi-layer error correction scheme that combines fountain and bi-level parity codes is used. As a prototype, fifteen origami encoded with ‘Data is in our DNA!\n’ are analyzed. Each origami encodes unique data-droplet, index, orientation, and error-correction information. The error-correction algorithms fully recover the message when individual docking sites, or entire origami, are missing. Unlike other approaches to DNA-based data storage, reading dNAM does not require sequencing. As such, it offers an additional path to explore the advantages and disadvantages of DNA as an emerging memory material.