Simplified Large-Scale Sanger Genome Sequencing for Influenza A/H3N2 Virus
Bibliographic record
Abstract
BACKGROUND: The advent of next-generation sequencing technologies and the resultant lower costs of sequencing have enabled production of massive amounts of data, including the generation of full genome sequences of pathogens. However, the small genome size of the influenza virus arguably justifies the use of the more conventional Sanger sequencing technology which is still currently more readily available in most diagnostic laboratories. RESULTS: We present a simplified Sanger-based genome sequencing method for sequencing the influenza A/H3N2 virus in a large-scale format. The entire genome sequencing was completed with 19 reverse transcription-polymerase chain reactions (RT-PCRs) and 39 sequencing reactions. This method was tested on 15 native clinical samples and 15 culture isolates, respectively, collected between 2009 and 2011. The 15 native clinical samples registered quantification cycle values ranging from 21.0 to 30.56, which were equivalent to 2.4×10(3)-1.4×10(6) viral copies/µL of RNA extract. All the PCR-amplified products were sequenced directly without PCR product purification. Notably, high quality sequencing data up to 700 bp were generated for all the samples tested. The completed sequence covered 408,810 nucleotides in total, with 13,627 nucleotides per genome, attaining 100% coding completeness. Of all the bases produced, an average of 89.49% were Phred quality value 40 (QV40) bases (representing an accuracy of circa one miscall for every 10,000 bases) or higher, and an average of 93.46% were QV30 bases (one miscall every 1000 bases) or higher. CONCLUSIONS: This sequencing protocol has been shown to be cost-effective and less labor-intensive in obtaining full influenza genomes. The constant high quality of sequences generated imparts confidence in extending the application of this non-purified amplicon sequencing approach to other gene sequencing assays, with appropriate use of suitably designed primers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".