Leveraging the MethMotif Toolkit to Characterize Context‐Specific Features and Roles of Methylation Sensitive Transcription Factors
Bibliographic record
Abstract
This article presents a comprehensive guide for using the MethMotif platform, which includes the MethMotif database, the TFregulomeR R package, and a new R library, Forked-TF, designed specifically for analyzing leucine-zipper transcription factors (TFs) that bind DNA as dimers. The MethMotif platform integrates transcription factor binding site (TFBS) motifs with DNA methylation profiles, providing an in-depth analysis of how methylation modulates TF binding across different cell types and conditions. The protocols are organized into three main workflows: (1) Exploration of transcription factor dimerization partners, (2) visualization of methylation-specific TF motifs using TFregulomeR, and (3) characterization of leucine-zipper TF binding patterns with a focus on dimerization. Using the platform's MethMotif database, users can retrieve ChIP-seq and DNA methylation data, intersect TFBS peak regions, and generate TFBS-methylation-informed motif logos. A case study of CEBPB in K562 cells is included to demonstrate the use of the platform, showing how to identify TF dimers, analyze their co-binding behavior, and visualize the impact of DNA methylation on binding specificity. The protocols also provide step-by-step instructions for software installation, data input formats, and interpretation of results, making it accessible to researchers with varying levels of computational expertise. Through these protocols, users can uncover how DNA methylation and TF dimerization influence gene regulatory networks, with a focus on leucine-zipper TFs in a cell-type-specific context. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Exploration of transcription factor dimerization partners Support Protocol 1: Software installation Support Protocol 2: Docker installation Support Protocol 3: Verifying installation Basic Protocol 2: Visualization of alternative cofactors Basic Protocol 3: Characterization of bZIP partners/cofactors Basic Protocol 4: Context-independent and context-dependent analysis.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".