The RUMIGEN EpiChip: a versatile, medium density DNA methylation Beadchip for large scale population studies in cattle
Bibliographic record
Abstract
Abstract Background DNA methylation contributes to the elaboration of phenotypes and is hypothesized to account for interindividual variations in farm animals. Currently, methodologies available to investigate DNA methylation in cattle rely on high throughput sequencing, which cannot be applied to large cohorts. To enable large scale DNA methylation analysis, we developed the RUMIGEN EpiChip, the first DNA methylation array specifically designed for cattle. Results Manufactured by Illumina, the EpiChip contains 43,317 CpG markers allowing analysis of phenotypes of agronomical interest as well as the study of regulatory processes. The assay design drew on data from numerous studies achieved by the scientific community, and includes CpGs where methylation varies with health status, physiological stage, fertility, and environmental challenges, as well as CpGs located in functional genomic elements (promoters, CTCF binding sites, expression quantitative trait loci). The technical performances of the EpiChip were tested on several semen and blood DNA samples and in two laboratories, showing excellent repeatability, accuracy and interoperability. Methylation values were also concordant with those obtained by reduced representation bisulfite sequencing. The EpiChip has demonstrated a good ability to explore biological processes such as genomic imprinting and differences between cell types, opening the possibility of inferring blood cell composition. Finally, analysis of longitudinal ear biopsies allowed accurate age prediction, suggesting the potential of the array to refine epigenetic clocks. Conclusions The RUMIGEN EpiChip is a cost-effective and versatile resource that opens new opportunities for the study of regulatory mechanisms underlying phenotypic variation and for large-scale association analyses in cattle.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".