Resuspension of DNA Sequencing Reaction Products in Agarose Increases Sequence Quality on an Automated Sequencer
Bibliographic record
Abstract
We are investigating approaches to increase DNA sequencing quality. Since a major factor in sequence generation is the cost of reagents and sample preparations, we have developed and optimized methods to sequence directly plasmid DNA isolated from alkaline lysis preparations. These methods remove the costly PCR and postsequencing purification steps but can result in low sequence quality when using standard resuspension protocols on some sequencing platforms. This work outlines a simple, robust, and inexpensive resuspension protocol for DNA sequencing to correct this shortcoming. Resuspending the sequenced products in agarose before electrophoresis results in a substantial and reproducible increase in sequence quality and read length over resuspension in deionized water and has allowed us to use the aforementioned sample preparation methods to cut considerably the overall sequencing costs without sacrificing sequence quality. We demonstrate that resuspension of unpurified sequence products generated from template DNA isolated by a modified alkaline lysis technique in low concentrations of agarose yields a 384% improvement in sequence quality compared to resuspension in deionized water. Utilizing this protocol, we have produced more than 74 000 high-quality, long-read-length sequences from plasmid DNA template on the MegaBACETM 1000 platform.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".