Genome graphs detect human polymorphisms in active epigenomic state during influenza infection
Bibliographic record
Abstract
Abstract Genetic variants, including mobile element insertions (MEIs), are known to impact the epigenome. We hypothesized that the use of a genome graph, which encapsulates genetic diversity, could reveal missing epigenomic signal. Given the contributions of mobile elements to the evolution of primate innate immunity, we tested this in monocyte-derived macrophages obtained from 35 individuals before and after Influenza virus infection. After characterizing genetic variants in this cohort using linked-reads, including 5140 Alu, 316 L1, 94 SVAs and 48 ERVs, we incorporated them into a genome graph. Mapping epigenetic data to this graph revealed 2.5%, 3.0% and 2.3% novel peaks for H3K4me1 and H3K27ac ChIP-seq and ATAC-seq respectively. Notably, using a genome graph also modified quantitative trait loci estimates and we observed 375 polymorphic MEIs in active epigenomic state. For example, we found an AluYh3 polymorphism whose chromatin state changed after infection and that was associated with the expression of TRIM25 , a gene that restricts influenza RNA synthesis. Our results demonstrate that graph genomes can reveal regulatory regions that would have been overlooked by other approaches.
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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 | 0.001 |
| 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) | 0.002 | 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".