Representing Transcription Factor Dimer Binding Sites Using Forked-Position Weight Matrices and Forked-Sequence Logos
Bibliographic record
Abstract
Abstract Current position weight matrices and sequence logos may not be sufficient for accurately modeling transcription factor binding sites recognized by a mixture of homodimer and heterodimer complexes. To address this issue, we developed forkedTF , an R-library that allows the creation of Forked-Position Weight Matrices (FPWM) and Forked-Sequence Logos (F-Logos), which better capture the heterogeneity of TF binding affinities based on interactions and dimerization with other TFs. Furthermore, we have enhanced the standard PWM format by incorporating additional information on co-factor binding and DNA methylation. Precomputed FPWM and F-Logos are made available in the MethMotif 2024 database, thereby providing ready-to-use resources for analyzing TF binding dynamics. Finally, forkedTF is designed to support the TRANSFAC format, which is compatible with most third-party bioinformatics tools that utilize PWMs. The forkedTF R-library is open source and can be accessed on GitHub at https://github.com/benoukraflab/forkedTF .
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".