Computational methods to explore chromatin state dynamics
Bibliographic record
Abstract
The human genome is marked by several singular and combinatorial histone modifications that shape the different states of chromatin and its three-dimensional organization. Genome-wide mapping of these marks as well as histone variants and open chromatin regions is commonly carried out via profiling DNA-protein binding or via chromatin accessibility methods. After the generation of epigenomic datasets in a cell type, statistical models can be used to annotate the noncoding regions of DNA and infer the combinatorial histone marks or chromatin states (CS). These methods involve partitioning the genome and labeling individual segments based on their CS patterns. Chromatin labels enable the systematic discovery of genomic function and activity and can label the gene body, promoters or enhancers without using other genomic maps. CSs are dynamic and change under different cell conditions, such as in normal, preneoplastic or tumor cells. This review aims to explore the available computational tools that have been developed to capture CS alterations under two or more cellular conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".