Self-Authenticating Genomic Materials with Advanced Genome Signatures
Bibliographic record
Abstract
Abstract The authenticity and integrity of synthetic genomic materials containing valuable intellectual property are essential for advancing scientific knowledge and enhancing biosafety. Existing DNA tags and watermarks, however, have limited efficacy due to low mutation tolerance and inadequate digital encoding capacity. Here, we present “Genome Signature”, a biochemically stable and tamper-resistant DNA labeling system that enables the creation of self-authenticating genomes. Central to this system is a novel Golomb-ruler-derived Genome-Comb, which efficiently maps extensive nucleotide sequences onto limited codons within endogenous genes, significantly improving error correction and data encoding across millions of nucleotides. Our method successfully recorded a 4.5 million-nucleotide genome in living E. coli . The Genome Signature autonomously identifies and corrects mutations and effectively encodes data within codon orders, ensuring genome integrity and authenticity. Furthermore, it allows precise tracking of sequences across different cells, potentially revolutionizing the development of reliable genomic materials in synthetic biology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".