Comparison of SARS-CoV-2 Indirect and Direct Detection Methods
Bibliographic record
Abstract
ABSTRACT The COVID-19 pandemic caused by the SARS-CoV-2 virus has placed extensive strain on RNA isolation and RT-qPCR reagents. Rapid development of new test kits has helped to alleviate these shortages. However, comparisons of these new detection systems are largely lacking. Here, we compare indirect methods that require RNA extraction, and direct RT-qPCR on patient samples. For RNA isolation we compared four different companies (Qiagen, Invitrogen, BGI and Norgen Biotek). For detection we compared two recently developed Taqman-based modules (BGI and Norgen Biotek), a SYBR green-based approach (NEB Luna Universal One-Step Kit) with published and newly-developed primers, and clinical results (Seegene STARMag RNA extraction system and Allplex 2019-nCoV RT-qPCR assay). Most RNA isolation procedures performed similarly, and while all RT-qPCR modules effectively detected purified viral RNA, the BGI system proved most sensitive, generating comparable results to clinical diagnostic data, and identifying samples ranging from 65 copies – 2.1×10 5 copies of viral Orf1ab/μl. However, the BGI detection system is ∼4x more expensive than other options tested here. With direct RT-qPCR we found that simply adding RNase inhibitor greatly improved sensitivity, without need for any other treatments (e.g. lysis buffers or boiling). The best direct methods were ∼10 fold less sensitive than indirect methods, but reduce sample handling, as well as assay time and cost. These studies will help guide the selection of COVID-19 detection systems and provide a framework for the comparison of additional systems.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".