Discovery, optimization and validation of an optimal DNA-binding sequence for the Six1 homeodomain transcription factor
Bibliographic record
Abstract
The Six1 transcription factor is a homeodomain protein involved in controlling gene expression during embryonic development. Six1 establishes gene expression profiles that enable skeletal myogenesis and nephrogenesis, among others. While several homeodomain factors have been extensively characterized with regards to their DNA-binding properties, relatively little is known of the properties of Six1. We have used the genomic binding profile of Six1 during the myogenic differentiation of myoblasts to obtain a better understanding of its preferences for recognizing certain DNA sequences. DNA sequence analyses on our genomic binding dataset, combined with biochemical characterization using binding assays, reveal that Six1 has a much broader DNA-binding sequence spectrum than had been previously determined. Moreover, using a position weight matrix optimization algorithm, we generated a highly sensitive and specific matrix that can be used to predict novel Six1-binding sites with highest accuracy. Furthermore, our results support the idea of a mode of DNA recognition by this factor where Six1 itself is sufficient for sequence discrimination, and where Six1 domains outside of its homeodomain contribute to binding site selection. Together, our results provide new light on the properties of this important transcription factor, and will enable more accurate modeling of Six1 function in bioinformatic studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".