VirusTaxo: Taxonomic classification of viruses from the genome sequence using k-mer enrichment
Bibliographic record
Abstract
Classification of viruses into their taxonomic ranks (e.g., order, family, and genus) provides a framework to organize an abundant population of viruses. Next-generation metagenomic sequencing technologies lead to a rapid increase in generating sequencing data of viruses which require bioinformatics tools to analyze the taxonomy. Many metagenomic taxonomy classifiers have been developed to study microbiomes, but it is particularly challenging to assign the taxonomy of diverse virus sequences and there is a growing need for dedicated methods to be developed that are optimized to classify virus sequences into their taxa. For taxonomic classification of viruses from metagenomic sequences, we developed VirusTaxo using diverse (e.g., 402 DNA and 280 RNA) genera of viruses. VirusTaxo has an average accuracy of 93% at genus level prediction in DNA and RNA viruses. VirusTaxo outperformed existing taxonomic classifiers of viruses where it assigned taxonomy of a larger fraction of metagenomic contigs compared to other methods. Benchmarking of VirusTaxo on a collection of SARS-CoV-2 sequencing libraries and metavirome datasets suggests that VirusTaxo can characterize virus taxonomy from highly diverse contigs and provide a reliable decision on the taxonomy of viruses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".