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MotifbreakR v2: extended capability and database integration.

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Summary
This summary is machine-generated.

MotifbreakR v2 predicts how genetic variants, including indels, disrupt transcription factor (TF) binding. It integrates TF binding data for more accurate predictions, enhancing variant effect analysis.

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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.