GHT-SELEX demonstrates unexpectedly high intrinsic sequence specificity and complex DNA binding of many human transcription factors
Bibliographic record
Abstract
Abstract There is ongoing debate regarding the degree to which transcription factors (TFs) independently specify genomic binding: TF binding motifs are typically short and degenerate, yielding many more binding site predictions than observed in cells. Here we present genomic high-throughput SELEX (GHT-SELEX)—a scalable method that surveys intrinsic binding of purified TFs to the fragmented, naked and unmodified genome. GHT-SELEX peaks for 179 diverse human TFs display surprisingly high overlap with chromatin immunoprecipitation sequencing peaks for the same TF. Comparable overlap can be obtained from motifs using appropriate analytical approaches. For C2H2 zinc finger (zf) proteins—the largest class of human TFs—GHT-SELEX shows that modular, alternative engagement of C2H2-zf domains is the norm, enabling several types of distinct target sites, and frequently involving internal duplication and divergence within the C2H2-zf array. Altogether, it is common for TFs to delineate a large fraction of in vivo genomic binding sites independently of other cellular factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".