Additional file 1 of Toward a base-resolution panorama of the in vivo impact of cytosine methylation on transcription factor binding
Bibliographic record
Abstract
Additional file 1: Fig S1-18. Figure S1. JAMS sequence coefficients for CTCF in HEK293 cells. Figure S2. TF-specific and background coefficients for CTCF in HEK293 cells. Figure S3. Likelihood ratio test per position to identify CTCF binding site positions with significant methylation effects. Figure S4. JAMS coefficients for CTCF across different cell lines. Figure S5. Calculating logFC S.E.M. threshold. Figure S6. Predicting differential CTCF binding independent of regional methylation. Figure S7. JAMS results by TF families. Figure S8. Example high-quality and low-quality JAMS models. Figure S9. Annotation of the zinc finger domains whose binding to DNA are affected by CpG methylation. Figure S10. In vivo methylation binding preferences of CEBBP and NFR1. Figure S11. Predicting differential binding of CEBPB across cell lines. Figure S12. Predicting differential binding of MAX across cell lines. Figure S13. Predicting differential binding of KAISO (ZBTB33) across cell lines. Figure S14. Comparison of methyl-sensitive positions identified by JAMS and bisulfite-SELEX. Figure S15. Methyl-plus and mixed-effect TFs identified by JAMS. Figure S16. Modeling choices for analysis of CTCF occupancy in HEK293 cells. Figure S17. Effect of mC12pG13 methylation on in vivo CTCF binding. Figure S18. Effect of mC12pG13 methylation on in vitro CTCF binding.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.844 | 0.243 |
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".