Motivation: Current position weight matrices and sequence logos may not be sufficient for accurately modeling transcription factor binding sites recognised by a mixture of homodimer and heterodimer complexes.
Results: 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 dimerisation 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 analysing TF binding dynamics. Finally, forkedTF is designed to support the TRANSFAC format, which is compatible with most third-party bioinformatics tools that utilise PWMs.
Availability: The forkedTF R-library is open source and can be accessed on GitHub at https://github.com/benoukraflab/forkedTF.

Co-authors: Roberto Tirado-Magallanes, Aida Ghayour-Khiavi, Quy Xiao Xuan Lin, Walter Santana, Hamid Usefi, Morgane Thomas-Chollier, Sudhakar Jha, Denis Thieffry, Touati Benoukraf