Additional file 2 of Respiratory syncytial virus (RSV) enhances translation of virus-resembling AU-rich host transcripts
Bibliographic record
Abstract
Supplementary Material 2: Supplementary Fig. S2. Related to Fig. 2. Quality control and GO analysis of RNA-seq samples.Western blot of total cytoplasmic protein obtained from samples used for high-throughput sequencing immunoblotted with polyclonal antibodyanti-RSV and monoclonal antibodies anti-RSV-N, anti-RSV-P and anti-RSV-M2-1 to confirm viral infection and loading control GAPDH.Agarose gel to determine RNA quality of purified RNA samplesselection). Note the absence of tRNAs in the polysomal RNA, unlike total RNA where free tRNA is abundant.Multidimensional scalingto determine similarity between RNAseq replicates. Diversity between samples is delineated by RNA typeand infection status.Heatmap demonstrating reproducibility between biological replicates. Color gradient shown on the heatmap corresponds to the Euclidian distance which was calculated for gene expression matrixes and compared between samples. Biological replicates are similar in distance and cluster together.IGV snapshot of the RSV genome of total RSV-infected samples. Individual viral mRNAs are annotated below each gene. Read coverage is specific to genomic regionsand absent in intergenic regions indicating specific sequencing of viral mRNAs as opposed to viral genome contamination. Some transcription readthrough exists between NS1 and NS2 as described earlier [43].Gene ontologyanalysis of biological processes for upregulated total mRNA, polysomal mRNA and TE during RSV infection. A large overlap exists between total and polysomal mRNAs indicating that transcripts that are increasing in abundance are also increased in the polysomes. On the other hand, the GO terms for TE are completely different.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.879 | 0.218 |
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".