Motif-Raptor

Motif-Raptor integrates sequence-based predictive models with chromatin accessibility, gene expression datasets, and GWAS summary statistics to evaluate how non-coding genetic variants affect transcription factor (TF) binding and downstream gene regulation.

Key Features:

• Integration of Multi-Omics Data: Combines sequence-based predictive models with chromatin accessibility data, gene expression datasets, and GWAS summary statistics to link variants to regulatory effects.
• TF-Centric Analysis: Focuses on transcription factors (TFs) to assess how genetic variants alter TF binding sites and influence gene regulation.
• Cell Type-Specificity: Identifies cell types in which trait-associated non-coding variants are likely to act, enabling context-dependent regulatory interpretation.
• Variant and TF Prioritization: Prioritizes regulatory TFs and non-coding single nucleotide polymorphisms (SNPs) for hypothesis generation about disease mechanisms and trait expression.

Scientific Applications:

• Complex trait and disease analysis: Applied to rheumatoid arthritis and red blood cell count to prioritize relevant cell types, regulatory TFs, and non-coding SNPs.
• Regulatory variant interpretation: Supports generation of hypotheses linking non-coding genetic variation to altered TF binding and phenotypic effects.

Methodology:

Combines sequence-based predictive models with chromatin accessibility data, gene expression datasets, and GWAS summary statistics; analyzes effects of variants on TF binding sites and gene regulation; identifies cell types where variants act; and prioritizes regulatory TFs and non-coding SNPs.