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DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation

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Information Density Enhancement Using Lossy Compression in DNA Data Storage.

Seongjun Seo¹, Anshula Tandon¹, Keun Woo Lee²

¹Department of Physics and Sungkyunkwan Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16419, Republic of Korea.

Advanced Materials (Deerfield Beach, Fla.)

|May 23, 2024

Summary

This summary is machine-generated.

New deoxyribonucleic acid (DNA) lossy compression models encode images into DNA sequences for high-density storage. Model B2 offers superior information density and image recovery, showing promise for biological data storage.

Keywords:

DNA data storage MNIST classification image quality assessment information density lossy compression

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High-Density DNA and RNA microarrays - Photolithographic Synthesis, Hybridization and Preparation of Large Nucleic Acid Libraries

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Published on: August 12, 2019

Area of Science:

Biotechnology
Data Storage
Bioinformatics

Background:

Traditional data storage faces limitations in density and longevity.
Deoxyribonucleic acid (DNA) offers a promising alternative for high-density data archiving.
Efficient compression algorithms are crucial for DNA-based data storage.

Purpose of the Study:

To develop and evaluate novel deoxyribonucleic acid (DNA) lossy compression models for image encoding.
To enhance information density and enable high-fidelity image recovery from DNA sequences.
To compare the performance of DNA compression models against established methods like Joint Photographic Experts Group (JPEG).

Main Methods:

Development of two DNA lossy compression models (Model A and Model B) for grayscale images.
Model A utilizes overlapped pixel domains with linear interpolation (LI).
Model B employs non-overlapped domains with nearest-neighbor interpolation (NNI).
Comparative analysis against JPEG compression using the Modified National Institute of Standards and Technology (MNIST) dataset.
Validation of image recognizability using convolutional neural network (CNN) performance.

Main Results:

DNA lossy compression models demonstrate competitive advantages over JPEG in information density and image restoration.
Model B2, a variant of Model B, achieves over 20 times the typical bit density per nucleotide.
Decompressed images from the MNIST dataset show high recognizability, validated by CNNs.
Model B2 effectively balances high information density with good image quality.

Conclusions:

DNA-based data storage systems offer a viable solution for high-density and efficient data compression.
The developed DNA lossy compression models, particularly Model B2, represent a significant advancement in biological data storage.
This research indicates a promising future for DNA as a medium for long-term, high-capacity data archiving.