Genome-wide analysis of histone modifications can contribute to the identification of candidate cis-regulatory regions in the threespine stickleback fish
Bibliographic record
Abstract
BACKGROUND: Cis-regulatory mutations often underlie phenotypic evolution. However, because identifying the locations of promoters and enhancers in non-coding regions is challenging, we have fewer examples of identified causative cis-regulatory mutations that underlie naturally occurring phenotypic variations than of causative amino acid-altering mutations. Because cis-regulatory elements have epigenetic marks of specific histone modifications, we can detect cis-regulatory elements by mapping and analyzing them. Here, we investigated histone modifications and chromatin accessibility with cleavage under targets and tagmentation (CUT&Tag) and assay for transposase-accessible chromatin-sequencing (ATAC-seq). RESULTS: Using the threespine stickleback (Gasterosteus aculeatus) as a model, we confirmed that the genes for which nearby regions showed active marks, such as H3K4me1, H3K4me3, and high chromatin accessibility, were highly expressed. In contrast, the expression levels of genes for which nearby regions showed repressive marks, such as H3K27me3, were reduced, suggesting that our chromatin analysis protocols overall worked well. Genomic regions with peaks of histone modifications showed higher nucleotide diversity within and between populations. By comparing gene expression in the gills of the marine and stream ecotypes, we identified several insertions and deletions (indels) with transposable element fragments in the candidate cis-regulatory regions. CONCLUSIONS: Thus, mapping and analyzing histone modifications can help identify cis-regulatory elements and accelerate the identification of causative mutations in the non-coding regions underlying naturally occurring phenotypic variations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".