MotifbreakR v2: extended capability and database integration

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Transcription factor (TF) binding is crucial for gene regulation.
• Genetic variants can alter TF binding affinity and gene expression.
• Accurate prediction of variant effects on TF binding is essential for understanding disease mechanisms.

Purpose of the Study:

• To update MotifbreakR software for analyzing complex genetic variants and improving TF binding disruption predictions.
• To integrate TF binding evidence from experimental datasets to enhance prediction accuracy.
• To provide a user-friendly R/Shiny interface for broader accessibility.

Main Methods:

• Utilized position weight matrices (PWMs) to assess variant impact on TF binding sites.
• Expanded variant analysis to include single nucleotide variants (SNVs) and insertions/deletions (indels).
• Integrated ReMap2022 database for experimental TF binding evidence and developed an R/Shiny GUI.

Main Results:

• MotifbreakR v2 now supports analysis of SNVs and indels, enabling assessment of more complex variant effects.
• Integration of ReMap2022 data provides experimental validation for predicted TF binding disruptions.
• The new R/Shiny interface simplifies the use of MotifbreakR for researchers.

Conclusions:

• MotifbreakR v2 offers a robust and enhanced tool for predicting the impact of genetic variants on TF binding.
• The incorporation of experimental binding data significantly improves the reliability of motif disruption predictions.
• The user-friendly interface promotes wider adoption and application in genomic research.