An inhibitor-free, versatile, fast, and cheap precipitation-based DNA purification method
Bibliographic record
Abstract
Nucleic acid purification is a key step in molecular biology workflows, and especially critical in synthetic biology. Two common techniques are phenol chloroform extraction and silica column adsorption. We have determined that commercial silica columns appear to elute a currently unidentified substance that can inhibit subsequent enzymatic reactions if not sufficiently diluted. To resolve this inhibition, we have developed a novel purification approach in which we achieve simultaneous protein removal and DNA precipitation through the application of chaotropic salts and alcohol/polyethylene glycol. While prior DNA precipitation approaches require 2 steps to remove protein and precipitate DNA, and 4 steps to remove RNA and precipitate DNA, our method accomplishes all of them in a single step. Our approach matches the speed and versatility of silica column purification while additionally being substantially cheaper, as well as avoiding restrictions on the maximum size of purified DNA fragments and the need for gel extraction to remove primer dimers below 700 bps. Our purification technique has also enabled us to uncover an important insight into nucleic acids: Gibson Assembly generates mainly linear DNA that transforms poorly into the cloning host E. coli, which is linked to suboptimal levels of functional colony formation after transformation. We show that decreasing the concentration of the linear DNA by 100-fold dramatically increases circularization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".