Extracting high-molecular weight DNA from cyanobacteria using Promega's WizardⓇ HMW DNA extraction kit with a modified protocol, METIS
Bibliographic record
Abstract
Extraction of high molecular weight (HMW) DNA for long read sequencing with little to no fragmentation and high purity is difficult to acquire from cyanobacterial species. Here we describe a modified method of extraction using Promega's Wizard Ⓡ HMW DNA Extraction Kit to acquire high molecular weight DNA from two cyanobacterial species. The protocol used in the kit is the "3.D. Isolating HMW DNA from Gram-Positive and Gram-Negative Bacteria" protocol. During a key step in the protocol, we propose that the lingering remnants of the cellular debris such as the mucilage layer of the cyanobacterial species is removed, preventing it from sticking to the DNA pellet produced. This customized modification is done between steps 11 and 12 and called METIS (maximizing extraction, transfer isopropanol step). This step drastically reduces the remaining mucilage layer, which if kept will stick to the DNA and make the DNA unsuitable for sensitive downstream next generation sequencing, like PacBio Sequencing. This protocol has been used to assemble two genomes from cyanobacteria ( Synechococcus sp. and Microcystis aeruginosa ) and one from a gram-negative bacterium, Lacibacter . It also allows for HMW DNA to be rapidly extracted without the use of toxic chemicals such as phenol and without extra reagents to be purchased. •Maximizing extraction, transfer isopropanol step (METIS) is the key modification during the step of DNA unraveling •METIS reduces leftover remnants of the mucilage layer in the extraction •High molecular weight DNA is produced with little to no fragmentation, and both a high purity and concentration
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.016 |
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".