Comparative study of Codon usage pattern and compositional distribution between whole genome and virulence gene set of <i>Vibrio cholerae N16961</i>
Bibliographic record
Abstract
Vibrio cholerae is the pathogenic organism causes cholera, a severe diarrheal disease. Occurs frequently in southern Asia. Vibrio cholerae has both pathogenic and nonpathogenic strains that vary in their virulence gene content. Great variety of strains and biotypes of Vibrio cholerae are found. These varieties are involve in shuffling of different pathogenic factors among them such as receiving and transferring genes for toxins, colonization factors, antibiotic resistance, capsular polysaccharides which giving resistance to chlorine 7 and new surface antigens, such as the 0139 lip polysaccharide and O antigen capsule. Different mode of transfer of these virulence gene i.e. lateral and horizontal transfer by phase, collection of pathogenic genes and other accessory genetic element, pave the way to understand how bacterial pathogen develop its Pathogenicity and become a new strain. To provide a insights into the genetic features and the relationship between the overall codon usage pattern of virulence gene set (VGS).We measure the GC content of VGS which shows that there is no any difference between GC content of whole genome and VGS .It also has been found that GC content shows the similar distribution among the CDS of both whole genome and Virulence gene set. A correlation analysis between the A3s, T3s, G3s, C3s, and GC3s, the ENC values, and the nucleotide contents (A%, T%, G%, C%, and GC %) indicated that mutational bias plays role in shaping the VGS codon usage bias.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".