Phylogenetic discordance can substantially overestimate genomic reassortment in avian influenza virus
Bibliographic record
Abstract
Abstract Recombination plays an important role in the evolution of RNA viruses, as it allows the exchange of genetic material between viral lineages. Reassortment, a form of recombination specific to segmented genomes, involves the exchange of entire segments and has contributed significantly to the adaptation and spread of influenza viruses through novel genomic combinations, i.e ., antigenic shifts. It is usually identified by phylogenetic discordance: differences in the topologies of trees reconstructed from different genomic segments. However, phylogenetic discordance can also result from error in reconstructing trees. To characterize the impact of reconstruction error, we curated a database of n = 11, 765 complete genomes of avian H5Nx influenza A viruses from avian hosts. We found evidence of widespread reassortment as measured by inferred subtree-prune-regraft (SPR) events, consistent with previous studies. Next, we ran replicate simulations of sequence evolution along the reference tree for the segment encoding hemagglutinin (HA), adjusting simulations for the lengths and clock rates of the other segments. These simulations provided a baseline for the expected amount of phylogenetic discordance in the absence of any reassortment. When sampling HA sequences at random from the database to build reference trees, we observed that simulating other segments without reassortment still yielded about 32% as many SPRs as the real segment data on average. The average proportion of SPRs without reassortment was greatly reduced (4%) if we selected an equivalent number of HA sequences retaining the most genetic diversity, which was consistent with the accuracy of phylogenetic reconstruction being the limiting factor. This implies that measuring reassortment by SPRs may have a high false positive rate, and that previous evidence of extensive reassortment in influenza viruses should be interpreted with caution. In addition, we observed that the SPRs reconstructed on simulated trees had significantly shorter distances between the prune and regraft locations than real trees. These results suggest that down-sampling sequences to maximize evolutionary divergence and filtering out the shortest SPRs may be effective measures against false positives.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".