Representing Transcription Factor Dimer Binding Sites Using Forked-Position Weight Matrices and Forked-Sequence Logos

Abstract

AbstractCurrent 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 developedforkedTF, 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 theMethMotif 2024database, thereby providing ready-to-use resources for analyzing TF binding dynamics. Finally,forkedTFis designed to support the TRANSFAC format, which is compatible with most third-party bioinformatics tools that utilize PWMs. TheforkedTFR-library is open source and can be accessed on GitHub athttps://github.com/benoukraflab/forkedTF.