Screening regulatory sequences from bacterial artificial chromosome DNA of alpha- and beta-globin gene clusters
Bibliographic record
Abstract
In the forthcoming postgenomic era, identification of regulatory DNA sequences is becoming increasingly important for characterizing DNA-binding proteins and for elucidating the regulatory mechanisms of gene expression. Presently, there lack efficient methods to broadly screen and identify DNA regulatory elements on a large scale. We established herein an efficient strategy to screen regulatory sequences from bacterial artificial chromosome (BAC) DNAs containing human alpha- and beta-globin gene clusters based on polymerase chain reaction and electrophoretic mobility shift assay (EMSA) techniques without purified transcription factors. Twenty-three subclones derived from alpha-BAC DNA by bulk EMSA selection retained the ability to bind nuclear proteins of K562 cells when retested by EMSA. In 19 clones sequenced, 14 are identical to those registered in GenBank and five have one base difference. All of the 24 randomly picked beta-BAC clones showed specific binding with nuclear proteins of K562 cells. In 11 clones sequenced, eight are identical to those registered in GenBank and three have one base difference. This approach could be particularly powerful if combined with other systematic methods for identifying cis-regulatory DNA elements.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".