A method for storing information in DNA with improved dropout tolerance
Bibliographic record
Abstract
Abstract Storing information in synthetic DNA oligomers is attractive for archival purposes due to the favorable physical density, stability, and energy efficiency of this storage medium. However, issues with this medium sometimes cause dropout ( i . e ., loss of oligomers) which may prevent the recovery of stored information. Here, an improved information storage method derived from the existing “DNA Fountain” method is reported. In this work we have developed and experimentally tested a robust algorithm to write digital data in pools of DNA strands by applying a rateless erasure code ( i . e ., fountain code), a Reed Solomon code, and an oligomer mapping code. Our new method includes changes to the fountain code, the oligomer mapping code, and the encoding and decoding processes. We have tested and benchmarked our algorithm vs similar algorithms and found that our method increases robustness to dropout, decreases encoding time, and decreases decoding time. The new method was validated in-vitro by successfully storing and recovering 105,360 bits of information. The advantages of the new method make it more appropriate for applications where information recovery is critical, where substantial sequence loss is expected, and/or where computational resources are limited. Furthermore, the inclusion of the novel oligomer mapping code enabled us to mitigate errors by restricting sequences of repeated bases and enhance security by eliminating start/stop codons, thus minimizing the risk of interaction with living cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".