DNA palette code for time-series archival data storage.

Area of Science:

• Biotechnology
• Data Storage
• Bioinformatics

Background:

• Long-term preservation of large, infrequently accessed cold data presents significant storage challenges.
• Deoxyribonucleic acid (DNA) offers a promising solution due to its exceptional stability and high storage density.
• Key metrics for DNA data storage efficiency include information density and decoding sequence coverage.

Purpose of the Study:

• To propose a novel coding scheme, the DNA palette code, optimized for cold data archival, particularly time-series datasets.
• To enhance the reliability and efficiency of DNA data storage for retrospective research applications.
• To demonstrate the effectiveness of the DNA palette code in achieving high net information density and low decoding sequence coverage.

Main Methods:

• Developed a coding scheme employing unordered combinations of index-free oligonucleotides for binary information representation.
• Conducted in vitro testing using clinical brain magnetic resonance imaging (MRI) data.
• Performed simulation validations on large-scale public MRI, planetary science, and meteorological datasets.

Main Results:

• The DNA palette code achieves high net information density and enables lossless decoding even with low sequencing coverage.
• The scheme demonstrates robustness against corrupted sequencing reads, allowing partial information recovery and preventing complete data loss.
• Validated applicability across diverse datasets including clinical MRI, planetary science, and meteorological data.

Conclusions:

• The DNA palette code is a highly effective solution for the long-term, high-density storage of cold data.
• It offers significant advantages in terms of information density, decoding efficiency, and data recovery reliability.
• The scheme shows broad applicability for archival storage of various large-scale datasets.