Bioinformatic and Physical Characterizations of Genome-Scale Ordered RNA Structure in Mammalian RNA Viruses
Bibliographic record
Abstract
By the analysis of thermodynamic RNA secondary structure predictions, we previously obtained evidence for evolutionarily conserved large-scale ordering of RNA virus genomes (P. Simmonds, A. Tuplin, and D. J. Evans, RNA 10:1337-1351, 2004). Genome-scale ordered RNA structure (GORS) was widely distributed in many animal and plant viruses, much greater in extent than RNA structures required for viral translation or replication, but in mammalian viruses was associated with host persistence. To substantiate the existence of large-scale RNA structure differences between viruses, a large set of alignments of mammalian RNA viruses and rRNA sequences as controls were examined by thermodynamic methods (to calculate minimum free energy differences) and by algorithmically independent RNAz and Pfold methods. These methods produced generally concordant results and identified substantial differences in the degrees of evolutionarily conserved, sequence order-dependent RNA secondary structure between virus genera and groups. A probe hybridization accessibility assay was used to investigate the physical nature of GORS. Transcripts of hepatitis C virus (HCV), hepatitis G virus/GB virus-C (HGV/GBV-C), and murine norovirus, which are predicted to be structured, were largely inaccessible to hybridization in solution, in contrast to the almost universal binding of probes to a range of unstructured virus transcripts irrespective of G+C content. Using atomic force microscopy, HCV and HGV/GBV-C RNA was visualized as tightly compacted prolate spheroids, while under the same experimental conditions the predicted unstructured poliovirus and rubella virus RNA were pleomorphic and had extensively single-stranded RNA on deposition. Bioinformatic and physical characterization methods both identified fundamental differences in the configurations of viral genomic RNA that may modify their interactions with host cell defenses and their ability to persist.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".