Personalized and graph genomes reveal missing signal in epigenomic data
Bibliographic record
Abstract
Abstract Background Epigenomic studies that use next generation sequencing experiments typically rely on the alignment of reads to a reference sequence. However, because of genetic diversity and the diploid nature of the human genome, we hypothesized that using a generic reference could lead to incorrectly mapped reads and bias downstream results. Results We show that accounting for genetic variation using a modified reference genome (MPG) or a denovo assembled genome (DPG) can alter histone H3K4me1 and H3K27ac ChIP-seq peak calls by either creating new personal peaks or by the loss of reference peaks. MPGs are found to alter approximately 1% of peak calls while DPGs alter up to 5% of peaks. We also show statistically significant differences in the amount of reads observed in regions associated with the new, altered and unchanged peaks. We report that short insertions and deletions (indels), followed by single nucleotide variants (SNVs), have the highest probability of modifying peak calls. A counter-balancing factor is peak width, with wider calls being less likely to be altered. Next, because high-quality DPGs remain hard to obtain, we show that using a graph personalized genome (GPG), represents a reasonable compromise between MPGs and DPGs and alters about 2.5% of peak calls. Finally, we demonstrate that altered peaks have a genomic distribution typical of other peaks. For instance, for H3K4me1, 518 personal-only peaks were replicated using at least two of three approaches, 394 of which were inside or within 10Kb of a gene. Conclusions Analysing epigenomic datasets with personalized and graph genomes allows the recovery of new peaks enriched for indels and SNVs. These altered peaks are more likely to differ between individuals and, as such, could be relevant in the study of various human phenotypes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".