Optimized respiratory virus influenza A whole genome sequencing strategies for improving even read coverage of segments
Bibliographic record
Abstract
Whole genome sequencing is increasingly being deployed to support respiratory virus influenza clinical studies and surveillance. However, PCR amplification inefficiency results in an imbalanced distribution among the eight segments, which makes it challenging to obtain complete genomes. The difficulties of amplifying the longer polymerase genes, particularly when the cycle threshold (Ct) of a specimen is greater than 25, were highlighted by the whole genome sequencing (WGS) data of 109 influenza A virus (IAV) specimens. We addressed the low genome coverage of the PB1 segment by incorporating additional singleplex PB1 primers into the WGS PCR assay, as well as balancing the ratio of forward primer variants targeting a single nucleotide polymorphism - either uracil (U) or cytosine (C) - located at the 4th position of the promoter region at the 3' terminus. Furthermore, we have verified the improved performance of the Invitrogen™ UniPrime™ enzyme when compared to its predecessor, SuperScript IV™, and developed a more efficient thermal cycling condition ("C") to generate eight complete IAV segments. Lastly, we determined that the optimal amplicon-to-bead volume ratio for removal of shorter, unwanted DNA fragments during PCR amplicon purification is 1:0.5. In summary, these optimizations improve the recovery of lower coverage segments and provide strategies aimed at obtaining high quality IAV genomes by means of Oxford Nanopore Technologies-based sequencing, ultimately providing valuable insights for better serving influenza clinical research and surveillance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".