Bibliographic record
Abstract
Diamond NCBI Genbank Viral database Database type: Diamond database Database format version: 3 Label: 2022-08-18_23-00-37 Sequences: 2,253,849 Sum length: 537,674,059 Assembly summary entries: 50,633 -------------------------------------------------------- SOVAP v.1.3: GitHub Soil Virome Analysis Pipeline Description The study of viral communities in complex environmental samples, such as soil, can provide valuable insights into the diversity and functions of viral communities in the ecosystem. However, processing and analyzing of virome data can be a challenging task that requires the integration of various computational tools and techniques. To address these challenges, we have developed SOVAP pipeline that utilizes a suite of state-of-the-art tools for processing, analysis, and annotation viromics and metagenomics data. It utilizes various tools such as Fastp and Centrifuge for preprocessing and contamination removal, geNomad, Diamond and Megan for identification and annotation of viral contigs which are assembled and clustered using Megahit and CD-HIT. Additionally, this pipeline provides an estimate of the abundance of viral contigs, allowing for a more comprehensive understanding of the virome within the sample. The integration of these tools offers a reliable and effective means of taxonomy classification and annotation of viral contigs, aiding researchers in gaining insight into the composition and function of the virome within the analyzed sample. By integrating the SOVAP pipeline with IMG/VR and geNomad, it is possible to identify a wider range of viruses, including those that were previously unknown. The batch-mode script allows for the processing of multiple datasets using the SOVAP pipeline. This feature is particularly useful for large-scale analyses, such as those involving multiple environmental samples or large sequencing datasets.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.367 | 0.445 |
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".