Dates of HIV infection can be estimated for seroprevalent patients by coalescent analysis of serial next-generation sequencing data
Bibliographic record
Abstract
OBJECTIVE: To reconstruct dates of HIV infection by the coalescent analysis of longitudinal next-generation sequencing (NGS) data. DESIGN: The coalescent predicts the time that has elapsed since the most recent common ancestor (MRCA) of a population. Because HIV tends to undergo severe bottlenecks upon transmission, the MRCA may be a good predictor of the time of infection. NGS provides an efficient means for performing large-scale clonal sequencing of HIV populations within patients, and the ideal raw material for coalescent analysis. METHODS: Baseline and follow-up plasma samples were obtained from 19 individuals enrolled into the Montréal Primary HIV Infection cohort. Dates of infection were initially estimated at baseline from nongenetic data (clinical and serological markers and patient questionnaires). HIV RNA was extracted and seven regions of the genome were amplified, subjected to parallel-tagged 454 pyrosequencing, and analyzed using the software package BEAST. RESULTS: Mean estimates of the time to the MRCA per patient were significantly correlated with nongenetic estimates (Spearman's ρ = 0.65, P = 4.4 × 10(-3)). The median absolute difference between coalescent and nongenetic date estimates was smallest (median 29.4 days) for highly variable regions of the HIV genome such as env V3, and greater (median 114.9 days) for more conserved regions such as pol. CONCLUSION: This application of NGS represents an important advancement, not only because accurate estimates of dates of infection can be derived retrospectively from archived specimens, but also because each analysis is patient-specific and, therefore, robust to variation in rates of HIV 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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".