Technical note: Comparative analyses of the quality and yield of genomic DNA from invasive and noninvasive, automated and manual extraction methods
Bibliographic record
Abstract
Several new automated methods have recently become available for high-throughput DNA extraction, including the Maxwell 16 System (Promega UK, Southampton, UK). The purpose of this report is to compare automated with manual DNA extraction methods, and invasive with noninvasive sample collection methods, in terms of DNA yield and quality. Milk, blood, and nasal swab samples were taken from 10 cows for DNA extraction. Nasal swabs were also taken from 10 calves and semen samples from 15 bulls for comparative purposes. The Performagene Livestock (DNA Genotek, Kanata, Ontario, Canada) method was compared with similar samples taken from the same animal using manual extraction methods. All samples were analyzed using both the Qubit Quantification Platform (Invitrogen Ltd., Paisley, UK) and NanoDrop spectrophotometer (NanoDrop Technologies, Inc., Wilmington, DE) to accurately assess DNA quality and quantity. In general, the automated Maxwell 16 System performed best, consistently yielding high quantity and quality DNA across the sample range tested. Average yields of 28.7, 10.3, and 19.2 μg of DNA were obtained from 450 μL of blood, 400 μL of milk, and a single straw of semen, respectively. The quality of DNA obtained from buffy coat and from semen was significantly higher with the automated method than with the manual methods (260/280 ratio of 1.9 and 1.8, respectively). Centrifugation of whole blood facilitated the concentration of leukocytes in the buffy coat, which significantly increased DNA yield after manual extraction. The Performagene method also yielded 18.4 and 49.8 μg of high quality (260/280 ratio of 1.8) DNA from the cow and calf nasal samples, respectively. These results show the advantages of noninvasive sample collection and automated methods for high-throughput extraction and biobanking of high quality DNA.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| 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".