Identifying tissue specific transcriptional regulatory elements in mammalian genomes
Bibliographic record
Abstract
Mammalian genomes are >98% non-coding and the function of most of this DNA remains unknown. Enhancers are one of the major components of the complex non-coding genome, which regulate gene expression in a tissue specific manner by binding transcription factors (TFs). Moreover, non-coding regulatory regions are critical for normal development, inter intra-species evolution and often inappropriately regulated in disease contexts, yet we have only a limited understanding of these regions at a sequence level. The analysis of known enhancers has failed to provide a sequence code of enhancers, how many and which specific TFs are required to build a functional enhancer. To better understand the transcriptional regulatory code, I identified embryonic stem (ES) cell enhancers using mouse-human comparative epigenomics and machine learning. I found genomic regions with conserved binding of multiple transcription factors (TFs) in mouse and human embryonic stem cells (ESCs) are enriched in a diverse repertoire of transcription factor binding sites (TFBS) including known and novel ESC regulators. The accuracy of these predictions was confirmed using enhancer reporter assays and site directed mutagenesis of conserved TFBS in natural enhancers as well as the construction of synthetic enhancers. As I found that natural enhancers have on average 12 unique conserved TFBS I hypothesized that >10 unique TFBS are important for regulatory activity. Multiple synthetic enhancers constructed from >10 unique TFBS had robust enhancer activity, whereas sequences containing
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".