COBRA improves the quality of viral genomes assembled from metagenomes
Bibliographic record
Abstract
Abstract Microbial and viral diversity, distribution, and ecological impacts are often studied using metagenome-assembled sequences, but genome incompleteness hampers comprehensive and accurate analyses. Here we introduce COBRA ( C ontig O verlap B ased R e- A ssembly), a tool that resolves de Bruijn graph based assembly breakpoints and joins contigs. While applicable to any short-read assembled DNA sequences, we benchmarked COBRA by using a dataset of published complete viral genomes from the ocean. COBRA accurately joined contigs assembled by metaSPAdes, IDBA_UD, and MEGAHIT, outcompeting several existing binning tools and achieving significantly higher genome accuracy (96.6% vs 19.8-59.6%). We applied COBRA to viral contigs that we assembled from 231 published freshwater metagenomes and obtained 7,334 high-quality or complete species-level genomes (clusters with 95% average nucleotide identity) for viruses of bacteria (phages), ∼83% of which represent new phage species. Notably, ∼70% of the 7,334 species genomes were circular, compared to 34% before COBRA analyses. We expanded genomic sampling of ≥ 200 kbp phages (i.e., huge phages), the largest of which was curated to completion (717 kbp in length). The improved phage genomes from Rotsee Lake provided context for metatranscriptomic data and indicated in situ activity of huge phages, WhiB and cysC / cysH encoding phages from this site. In conclusion, COBRA improves the assembly contiguity and completeness of microbial and viral genomes and thus, the accuracy and reliability of analyses of gene content, diversity, and evolution.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".