Modified low‐salt CTAB extraction of high‐quality DNA from contaminant‐rich tissues
Bibliographic record
Abstract
The increasing use of high-throughput sequencing platforms has made the isolation of pure, high molecular weight DNA a primary concern for studies of a diverse range of organisms. Purification of DNA remains a significant challenge in many tissue and sample types due to various organic and inorganic molecules that coprecipitate with nucleic acids. Molluscs, for example, contain high concentrations of polysaccharides which often coprecipitate with DNA and can inhibit downstream enzymatic reactions. We modified a low-salt CTAB (MoLSC) extraction protocol to accommodate contaminant-rich animal tissues and compared this method to a standard CTAB extraction protocol and two commercially available animal tissue DNA extraction kits using oyster adductor muscle. Comparisons of purity and molecular integrity showed that our in-house protocol yielded genomic DNA generally free of contaminants and shearing, whereas the traditional CTAB method and some of the commercial kits yielded DNA unsuitable for some applications of massively parallel sequencing. Our open-source MoLSC protocol provides a cost-effective, scalable, alternative DNA extraction method that can be easily optimized and adapted for sequencing applications in other contaminant-rich samples.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".