Pantheon-DNA: Versatile encoding-decoding system with integrated adaptive NGS preprocessing algorithms for DNA data storage
Bibliographic record
Abstract
We introduce Pantheon-DNA, an end-to-end processing pipeline for DNA data storage that effectively addresses scalability challenges while efficiently managing large datasets, maintaining ≥99.996% retrievability at 10× coverage under both LER and HER in our tests. To prevent repetitive patterns in DNA sequences, which potentially cause chimeras at the molecular level and also hinder clustering algorithms, we propose a data arrangement scheme and a randomization procedure during encoding. We use block data architecture to enhance parallel processing and retrieval. The proposed sequencing data preprocessing pipeline utilizes prior knowledge of the data structure encoded in the DNA sequences to simplify conventional clustering routines and reduce computational complexity. The system's robustness and reliability are validated through an actual synthesis and sequencing experiment, which encodes and decodes 1.59 MB of data containing multiple files. Future enhancements will focus on refining error correction capabilities, particularly for indel recovery, as well as optimizing preprocessing efficiency and sensitivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".