Identifying RNA secondary structures in the SARS-CoV-2 viral genome
Bibliographic record
Abstract
Motivation: SARS-CoV-2 is the virus responsible for the COVID-19 pandemic that currently impacts our world. SARS-CoV-2 is an enveloped, positive sense single stranded RNA virus and like other RNA viruses is known to form RNA secondary structure in its genome. In related viruses the secondary structures are responsible for fulfilling roles including proper expression of viral gene products and possibly regulation of viral genome replication. I hypothesize that SARS-CoV-2 may be capable of forming additional secondary structures beyond what is already known and that those secondary structures are identifiable on the basis of sequence conservation with related RNA viruses. Results: By repurposing and expanding an existing computational pipeline de- signed for the detection of structural RNAs in vertebrates, I identified 40 regions of the SARS-CoV-2 genome highly likely to form secondary structure. Partial re- identification of known secondary structures in the SARS-CoV-2 genome was achieved. To further explore the role these structures may fill, the 9 most conservatively pre- dicted structures were analyzed in wild viral samples collected from three Canadian provinces, and distinct patterns of mutation were observed. The 40 regions identi- fied by my modified pipeline were compared against three contemporary works and the differences between findings were quantified. Lastly, Variants of Concern for SARS-CoV-2 were analyzed for prevalent but poorly reported mutations that may influence RNA secondary structure. Code developed for this work is available at https://github.com/aziesel/MSc.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".