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  3. Ultra-fast Variant Effect Prediction Using Biophysical Transcription Factor Binding Models.
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  3. Ultra-fast Variant Effect Prediction Using Biophysical Transcription Factor Binding Models.

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

Rezwan Hosseini1, Ali Tugrul Balci2, Dennis Kostka1

  • 1Department of Computation and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15213, United States.

Nucleic Acids Research
|October 9, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces motifDiff, a computational tool that accurately predicts the functional impact of genetic variants on transcription factor binding sites. It enhances variant effect prediction, especially for common variants, by using biophysical models.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Sequence variations in transcription factor (TF)-binding sites impact gene expression and disease.
  • Current deep learning models struggle with accurate variant effect prediction, particularly for common genetic variants.
  • Biophysical models of TF binding are crucial for interpreting variant effects and gaining mechanistic insights.

Purpose of the Study:

  • Introduce motifDiff, a novel computational tool for quantifying variant effects on TF-DNA interactions.
  • Enhance the accuracy and interpretability of variant effect prediction using biophysical models.
  • Provide a scalable and statistically rigorous method for analyzing millions of genetic variants.

Main Methods:

  • Developed motifDiff using mono- and dinucleotide position weight matrices.
  • Implemented a statistically rigorous normalization strategy for optimal performance.
  • Evaluated motifDiff on diverse ground truth datasets of in vivo common variant effects.

    • Main Results:

    • motifDiff demonstrates high efficacy in predicting variant effects across multiple datasets.
    • The tool provides scalable analysis, scoring millions of variants rapidly.
    • Established robust benchmarks for variant effect prediction accuracy.

    Conclusions:

    • motifDiff offers a significant advancement in predicting the functional impact of genetic variants.
    • The tool enhances mechanistic understanding of TF-DNA interactions and gene regulation.
    • motifDiff provides unique insights into human accelerated regions and variant interpretation.