Impact of cobas PCR Media Freezing on SARS-CoV-2 Viral RNA Integrity and Whole Genome Sequencing Analyses
Bibliographic record
Abstract
ABSTRACT SARS-CoV-2 whole genome sequencing is an important molecular biology tool performed to support many aspects of the response to the pandemic. Freezing of primary clinical nasopharyngeal swab samples and shipment to reference laboratories is usually required since RNA sequencing is rarely available in routine clinical microbiology laboratories where initial diagnosis and support to outbreak investigations occur. The cobas PCR Media transport medium developed by Roche facilitates high throughput analyses on cobas multianalyzer PCR platforms. There is no data on the stability of SARS-CoV-2 RNA after freezing and thawing of clinical samples in this transport medium, but potential denaturing of the molecular template could impair test results. Our objective was to compare the quality and results of SARS-CoV-2 genomic sequencing when performed on fresh or frozen samples in cobas PCR Media. Viral whole genome sequencing was performed using Oxford Nanopore Technologies MinION platform. Genomic coverage and sequencing depth did not significantly differ between fresh and frozen samples (n=10). For samples with lower viral inoculum and PCR cycle threshold above 30, sequencing quality scores and detection of single nucleotide polymorphisms did not differ either. Freezing of cobas PCR Media does not negatively affect the quality of SARS-CoV-2 RNA sequencing results and it is therefore a suitable transport medium for outsourcing sequencing analyses to reference laboratories. Those results support secondary use of diagnostic nasopharyngeal swab material for viral sequencing without requirement for additional clinical 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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".