regSNPs-ASB

regSNPs-ASB applies a generalized linear model to ATAC-seq read counts to identify regulatory single-nucleotide polymorphisms (SNPs) that drive allele-specific transcription factor binding and thereby affect gene expression.

Key Features:

• Generalized Linear Model: Employs a generalized linear model to analyze ATAC-seq data at heterozygous loci for detecting allele-specific signals.
• Input Data: Uses raw read counts from ATAC-seq experiments to assess chromatin accessibility and transposase-cleavage patterns.
• Regulatory SNP Identification: Detects SNPs in transcription factor binding sites by identifying differential transposase-cleavage patterns indicative of preferential allele binding.
• Multi-omics Integration: Integrates identified SNPs with RNA-seq and publicly available chromatin interaction datasets to associate variants with potential target genes.
• eQTL Validation: Compares identified regulatory SNPs against eQTLs from the Genome-Tissue Expression (GTEx) Project database to evaluate overlap with expression-associated variants.

Scientific Applications:

• Functional Genomics: Pinpoints causal regulatory variants within noncoding regions to elucidate mechanisms of gene regulation.
• Disease Research: Identifies SNPs that alter transcription factor binding to support studies of genetic contributions to complex diseases.
• Therapeutic Target Development: Prioritizes regulatory variants and associated target genes that may inform development of targeted interventions.

Methodology:

Applies a generalized linear model to raw ATAC-seq read counts at heterozygous loci to detect differential transposase-cleavage patterns indicative of allele-specific transcription factor binding, integrates results with RNA-seq and chromatin interaction datasets, and compares identified SNPs to GTEx eQTLs.