Sanger and Next-Generation Sequencing data for characterization of CTL epitopes in archived HIV-1 proviral DNA
Bibliographic record
Abstract
One of the strategies for curing viral HIV-1 is a therapeutic vaccine involving the stimulation of cytotoxic CD8-positive T cells (CTL) that are Human Leucocyte Antigen (HLA)-restricted. The lack of efficiency of previous vaccination strategies may have been due to the immunogenic peptides used, which could be different from a patient's virus epitopes and lead to a poor CTL response. To counteract this lack of specificity, conserved epitopes must be targeted. One alternative is to gather as many data as possible from a large number of patients on their HIV-1 proviral archived epitope variants, taking into account their genetic background to select the best presented CTL epitopes. In order to process big data generated by Next-Generation Sequencing (NGS) of the DNA of HIV-infected patients, we have developed a software package called TutuGenetics. This tool combines an alignment derived either from Sanger or NGS files, HLA typing, target gene and a CTL epitope list as input files. It allows automatic translation after correction of the alignment obtained between the HxB2 reference and the reads, followed by automatic calculation of the MHC IC50 value for each epitope variant and the HLA allele of the patient by using NetMHCpan 3.0, resulting in a csv file as output result. We validated this new tool by comparing Sanger and NGS (454, Roche) sequences obtained from the proviral DNA of patients at success of ART included in the Provir Latitude 45 study and showed a 90% correlation between the quantitative results of NGS and Sanger. This automated analysis combined with complementary samples should yield more data regarding the archived CTL epitopes according to the patients' HLA alleles and will be useful for screening epitopes that in theory are presented efficiently to the HLA groove, thus constituting promising immunogenic peptides for a therapeutic vaccine.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".