DNaseI and flowcell clearing for increasing long read yields and multi-sample sequencing v1
Bibliographic record
Abstract
DNaseI and flowcell clearing for increasing long read yields and multi-sample sequencing The use of the Nuclease flush protocol (DNaseI digestion of DNA on the surface of the flowcell) has become an integral part of our long reads sequencing efforts. These can be seen in the figure above (Protocol 'Modified LSK109 ligation prep with needle shear and bead clean up') and can be seen as increases in data yield accumulations on the curves. In the case of our native rat genomic sequencing we see an accumulation of pore blockage over time that is DNA fragment length dependent. Shown below are two libraries produced from an identical sample sheared to an increasingly smaller size and run. Using DNaseI surface clearing we often see recovery of an additional 50 – 80 % of total reported pores on top of what a mux scan reports before treatment as long as the flowcell surface has not been “damaged” in some other way. You can monitor the process live if you restart a run and then pipette in the DNaseI clearing solution and watch the duty plots (see below). Once complete in ~30 mins flushing back to FLB/PFB and re-tethering will on a restart produce a pore count of what you have left to use. The flowcell is “reset” ready for the next library addition and run restart. That may be the same library again or something different…… With our “blocky” rat genomic samples sheared to 18-34kb we have been performing these resets every 16 – 24 hrs. For larger fragment lengths we will likely shorten this time period so the flowcell is not sitting there in an unproductive “blocked” state. It might be time for some MinKNOW scripted automated yield monitoring and hardware surface clearing hacks ;o).
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.072 | 0.057 |
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".