BayesPI-BAR2

BayesPI-BAR2 predicts functional non-coding somatic mutations that alter transcription factor binding to assess regulatory disturbances in cancer cohorts.

Key Features:

• Integrative analysis: Integrates gene expression profiles, spatial distributions of somatic mutations, and biophysical models estimating protein-DNA binding affinity.
• Multiple mutations and patients: Accounts for multiple mutations within individual genomes and aggregates information across patients.
• Promoter-region focus: Analyzes sequence variants in gene promoter regions to predict alterations in transcription factor (TF) binding.
• Biophysical TF binding prediction: Uses biophysical affinity models to estimate changes in protein-DNA binding caused by sequence variants.
• Batch processing and WGS support: Processes multiple genome-wide mutation datasets simultaneously, including whole-genome sequencing data.
• Evaluation on cancer datasets: Has been evaluated using follicular lymphoma and skin cancer patient datasets to identify TFs affected by non-coding mutations.

Scientific Applications:

• Identification of functional regulatory mutations: Detection of non-coding somatic mutations that disrupt protein-DNA interactions and regulatory elements.
• Cohort-level discovery of altered TF binding: Discovery of transcription factors with altered binding across patient cohorts due to non-coding mutations.
• Promoter regulatory inference: Assessment of promoter variants' impact on transcriptional regulation.
• Prioritization for validation and intervention: Prioritization of candidate regulatory mutations and TFs for downstream experimental validation and therapeutic target investigation.

Methodology:

Combines gene expression profiles, spatial distributions of somatic mutations, and biophysical models of protein-DNA binding affinity while considering multiple mutations per genome and across patients to predict transcription factor binding changes in promoter regions and genome-wide mutation datasets.