Conservative taxonomy and quality assessment of giant virus genomes with GVClass
Bibliographic record
Abstract
Abstract Background Large double-stranded DNA viruses of the phylum Nucleocytoviricota (Giant viruses; GVs) include the largest known viruses, both in terms of capsid and genome size and are associated with a wide range of eukaryotic hosts. The ones able to infect protists and algae have been shown to be the dominant orders of GVs in the environmental samples. These viruses encode for genes that may have significantly impacted biogeochemical cycling and host genome evolution. While GVs are frequently found in environmental sequence data, their large and complex genomes, composed of genes acquired from various cellular lineages, pose challenges for their identification and taxonomic classification. Results We present GVClass, a tool that identifies giant viruses in sequence data and provides taxonomic assignments, and estimates for genome completeness and contamination. GVClass performs gene calling optimized for giant viruses and utilizes a conservative approach based on consensus single protein phylogenies for robust taxonomic assignments. The genes used for classification represent highly conserved giant virus orthologous groups and low copy number cellular and viral panorthologs. In our benchmarking, GVClass demonstrated high quality and accurate taxonomic assignment of giant virus sequences. GVClass showed high to very high precision, with over 90% of tested instances correctly predicted at the genus level and near-perfect prediction (>99%) at higher taxonomic ranks (family, order, class). Conclusion In the light of rapidly increasing amounts of sequence data and associated metagenome-assembled genomes, GVClass provides a conservative approach to identify, classify and quality-check giant virus genomes, which with other methods often remained unassigned or misclassified using other methods. GVClass has already been used through viral meta-analysis and to benchmark the viral sequences detection pipeline geNomad. The standalone version is freely available and it has been integrated in the Integrated Microbial Genomes / Virus database (IMG/VR), offering the opportunity to upload user data for giant virus classification.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".