NeoRdRp2 with improved seed data, annotations, and scoring
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
RNA-dependent RNA polymerase (RdRp) is a marker gene for RNA viruses; thus, it is widely used to identify RNA viruses from metatranscriptome data. However, because of the high diversity of RdRp domains, it remains difficult to identify RNA viruses using RdRp sequences. To overcome this problem, we created a NeoRdRp database containing 1,182 hidden Markov model (HMM) profiles utilizing 12,502 RdRp domain sequences. Since the development of this database, more RNA viruses have been discovered, mainly through metatranscriptome sequencing analyses. To identify RNA viruses comprehensively and specifically, we updated the NeoRdRp by incorporating recently reported RNA viruses. To this end, 557,197 RdRp-containing sequences were used as seed RdRp datasets. These sequences were processed through deduplication, clustering, alignment, and splitting, thereby generating 19,394 HMM profiles. We validated the updated NeoRdRp database, using the UniProtKB dataset and found that the recall and specificity rates were improved to 99.4% and 81.6%, from 97.2% and 76.8% in the previous version, respectively. Comparisons of eight different RdRp search tools showed that NeoRdRp2 exhibited balanced RdRp and nonspecific detection power. Expansion of the annotated RdRp datasets is expected to further accelerate the discovery of novel RNA viruses from various transcriptome datasets. The HMM profiles of NeoRdRp2 and their annotations are available at https://github.com/shoichisakaguchi/NeoRdRp .
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.
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 it