PTF-Vac: An explainable and generative deep co-learning encoder-decoder system for ab initio discovery of plant transcription factor binding sites.

ABSTRACT: Discovery of transcription factor (TF) binding sites (TFBSs) and their motifs in plants poses significant challenges due to high cross-species variability. The interactions between TFs and their binding sites are highly specific and context dependent. Most existing TFBS-finding tools are not sufficiently accurate to discover these binding sites in plants. They fail to capture cross-species variability, interdependence between TF structures and corresponding TFBSs, and the context specificity of binding. Because they are coupled to a predefined TF-specific model/matrix, they are strongly influenced by the volume and quality of data provided to build the motifs. All these software applications make the assumption that the user input is specific to a particular TF, limiting their use for prac

SUBMITTER: Gupta S 

PROVIDER: S-EPMC12785152 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature