Deriving the Upstream Truncation Thresholds of Human Promoter Sequences Based on Expression Correlation
Bibliographic record
Abstract
Most promoter sequences are generated by arbitrary truncating regions upstream from the transcription start site, typically around 1,000 base pairs. However, this arbitrary approach might miss essential regulatory elements required for proper transcription. Unfortunately, there is no widely accepted rationale for choosing a specific promoter truncation threshold, despite its importance in obtaining expected expression profiles. In this study, we present a data-driven method to define biologically meaningful promoter thresholds. We begin by identifying putative shared promoters between neighboring gene pairs and classify them as true shared promoters based on expression similarity using GTEx data. Our analysis reveals that shorter putative shared promoters are more likely to be true shared promoters and thus lead to similar expression profiles. Using the lengths of these true shared promoters, we subsequently define aggressive, standard, and conservative thresholds for human promoter sequences. We further refine these thresholds using known cis-regulatory elements and transcription factor binding regions, resulting in more compact promoter boundaries. These refined thresholds provide a biologically grounded alternative to arbitrary values and establish a foundation for future experimental validation and practical applications. Moreover, our refined thresholds are especially well-suited for scenarios where compact promoter design is critical.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".