Local DNA shape is a general principle of transcription factor binding specificity in Arabidopsis thaliana.

ABSTRACT: Understanding gene expression will require understanding where regulatory factors bind genomic DNA. The frequently used sequence-based motifs of protein-DNA binding are not predictive, since a genome contains many more binding sites than are actually bound and transcription factors of the same family share similar DNA-binding motifs. Traditionally, these motifs only depict sequence but neglect DNA shape. Since shape may contribute non-linearly and combinational to binding, machine learning approaches ought to be able to better predict transcription factor binding. Here we show that a random forest machine learning approach, which incorporates the 3D-shape of DNA, enhances binding prediction for all 216 tested Arabidopsis thaliana transcription factors and improves the resolution of differe

SUBMITTER: Sielemann J 

PROVIDER: S-EPMC8590021 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature