NeoRdRp: A comprehensive dataset for identifying RNA-dependent RNA polymerase of various RNA viruses from metatranscriptomic data
Bibliographic record
Abstract
Abstract RNA viruses are distributed throughout various environments, and most RNA viruses have recently been identified by metatranscriptome sequencing. However, due to the high nucleotide diversity of RNA viruses, it is still challenging to identify novel RNA viruses from metatranscriptome data. To overcome this issue, we created a dataset of RNA-dependent RNA polymerase (RdRp) domains that are essential for all RNA viruses belonging to Orthornavirae . Genes with RdRp domains from various RNA viruses were clustered based on their amino acid sequence similarity. For each cluster, a multiple sequence alignment was generated, and a hidden Markov model (HMM) profile was created if the number of sequences was greater than three. We further refined the 426 HMM profiles by detecting the RefSeq RNA virus sequences and subsequently combined the hit sequences with the RdRp domains. As a result, a total of 1,182 HMM profiles were generated from 12,502 RdRp domain sequences, and the dataset was named NeoRdRp. Almost all NeoRdRp HMM profiles successfully detected RdRp domains, specifically in the UniProt dataset. Furthermore, we compared the NeoRdRp dataset with two previously reported methods for RNA virus detection using metatranscriptome sequencing data. Our methods successfully identified most of the RNA viruses in the datasets; however, some RNA viruses were not detected, as in the cases of the other two methods. The NeoRdRp can be repeatedly improved by adding new RdRp sequences and is applicable as a system for detecting various RNA viruses from diverse metatranscriptome data.
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.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".