Ultra-fast variant effect prediction using biophysical transcription factor binding models.

ABSTRACT: Sequence variation within transcription factor (TF)-binding sites can significantly affect TF-DNA interactions, influencing gene expression and contributing to disease susceptibility or phenotypic traits. Despite recent progress in deep sequence-to-function models that predict functional output from sequence data, these methods perform inadequately on some variant effect prediction tasks, especially with common genetic variants. This limitation underscores the importance of leveraging biophysical models of TF binding to enhance interpretability of variant effect scores and facilitate mechanistic insights. We introduce motifDiff, a novel computational tool designed to quantify variant effects using mono- and dinucleotide position weight matrices. motifDiff offers several key advantages, inc

SUBMITTER: Hosseini R 

PROVIDER: S-EPMC12507518 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature