Nucleic acid contamination of glycogen used in nucleic acid precipitation and assessment of linear polyacrylamide as an alternative co-precipitant
Bibliographic record
Abstract
Molecular-grade glycogen is widely used to recover nanogram or picogram quantities of DNA and RNA across molecular biology applications in the life sciences. As a result, its purity is critical to obtain reliable results. Using agarose gel electrophoresis, we detected pg/microL (DNA) to ng/microL (RNA) concentrations of nucleic acid in two of the nine glycogen samples obtained from commercial suppliers. Denaturing gradient gel electrophoresis of 16S rRNA gene PCR-amplified products indicated that an additional two samples contained detectable contamination. We also tested a synthetic polymer co-precipitant, linear polyacrylamide (LPA); none of the four samples tested with LPA were detectably contaminated. The partial 16S rRNA gene sequence associated with the contaminated samples of the shellfish-derived glycogen was nearly identical to the sequence of Actinobacteria lwoffii, which has been isolated from mussels previously. By testing the recovery of low-nanogram amounts of DNA with multiple precipitants and simulated experimental conditions, we demonstrated that LPA was a preferable co-precipitant for sensitive protocols.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".