Computational analysis of transcriptional regulation from local sequence features to three dimensional chromatin domains
Bibliographic record
Abstract
Regulation of gene expression spans different levels of complexity: from genomic sequence, transcription factor binding and epigenetics, to three-dimensional chromatin interactions. Data from different individuals such as genetic variations presents an extra dimension to consider. Abnormal activities at any level may lead to disease phenotypes, motivating deeper exploration of gene regulation. New high-throughput sequencing techniques have empowered genome-wide studies of the regulatory mechanisms within cells. This thesis uses computational approaches to examine gene regulation with high-throughput data in order to address biological hypotheses traversing from short local sequence features to megabase-sized topologically associating domains (TADs). The hypotheses addressed in the thesis have two central themes: 1) the elucidation of local and domain regulation of gene expression, and 2) the application of such knowledge to identify functional phenotypic variants. We developed a computational approach to identify functional variants associated with cancer, and demonstrated how annotating regulatory sequences and linking these regions to target genes can strengthen genome interpretation. The concurrent and intertwined nature of local and domain regulation of gene expression develops as the thesis unfolds. In a study of genes that escape from X-chromosome inactivation, we found the YY1 transcription factor to be a key regulator, and is potentially associated with long distance chromatin looping mechanisms. Similarly, when studying the spread of inactivation to the autosomes in translocated cells, we detected local features associated with inactivation status, and at the domain level, we observed the spreading to be in accordance with TADs. Lastly, when considering TADs as transcriptional units, the identification of cell type-selectively co-expressed and co-localized TADs highlighted an organized and dynamic chromatin architecture across multiple cell types. In summary, this thesis provides insights into the mechanisms involved in gene expression across multiple scales (from local sequences to chromatin domains) using computational analyses on publicly available datasets. The presented methods and results have potential applications to interpret genetic variations and further our understanding in diseases and phenotypes. The findings may contribute to an era of preventative and regenerative medicine to come.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".