Characterization of Hepatitis C Virus Recombination in Cameroon by Use of Nonspecific Next-Generation Sequencing
Bibliographic record
Abstract
The importance of recombination in the evolution and genetic diversity of the hepatitis C virus (HCV) is currently uncertain. Only a small number of intergenotypic recombinants have been identified so far, and each has core and envelope genes classified as belonging to genotype 2. Here, we investigated two putative genotype 4/1 recombinants from southern Cameroon using a number of approaches, including standard Sanger sequencing, genotype-specific PCR amplification, and non-HCV-specific Illumina RNA sequencing (RNA-seq). Recombination between genotypes 1 and 4 was confirmed in both samples, and the parental lineages of each recombinant belong to HCV subtypes that are cocirculating at a high prevalence in Cameroon. Using the RNA-seq approach, we obtained a complete genome for one sample, which contained a recombination breakpoint at the E2/P7 gene junction. We developed and applied a new method, called Deep SimPlot, which can be used to visualize and identify viral recombination directly from the short sequence reads created by next-generation sequencing in conjunction with a consensus sequence.
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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 | 0.001 |
| 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".