Transcription factor binding at enhancers: shaping a genomic regulatory landscape in flux
Bibliographic record
Abstract
The mammalian genome is packed tightly in the nucleus of the cell. This packing is primarily facilitated by histone proteins and results in an ordered organization of the genome in chromosome territories that can be roughly divided in heterochromatic and euchromatic domains. On top of this organization several distinct gene regulatory elements on the same chromosome or other chromosomes are thought to dynamically communicate via chromatin looping. Advances in genome-wide technologies have revealed the existence of a plethora of these regulatory elements in various eukaryotic genomes. These regulatory elements are defined by particular in vitro assays as promoters, enhancers, insulators, and boundary elements. However, recent studies indicate that the in vivo distinction between these elements is often less strict. Regulatory elements are bound by a mixture of common and lineage-specific transcription factors which mediate the long-range interactions between these elements. Inappropriate modulation of the binding of these transcription factors can alter the interactions between regulatory elements, which in turn leads to aberrant gene expression with disease as an ultimate consequence. Here we discuss the bi-modal behavior of regulatory elements that act in cis (with a focus on enhancers), how their activity is modulated by transcription factor binding and the effect this has on gene regulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".