SNP2TFBS

SNP2TFBS identifies how single nucleotide polymorphisms (SNPs) affect transcription factor (TF) binding sites in the human genome to investigate regulatory variation.

Key Features:

• In Silico Approach: Compares the reference human genome assembly (hg19) with alternate assemblies that incorporate common genetic variants from the 1000 Genomes Project (allele frequency ≥ 0.001) to predict changes in TF binding.
• Transcription Factor Binding Score: Calculates TF binding scores for both reference and alternate genomes to identify SNPs that abolish, create, or alter TF binding affinities.
• Position Weight Matrix Model: Uses position weight matrices (PWMs) to estimate the impact of SNPs on TF binding specificity.
• Annotated Database Files: Provides text files containing detailed annotations for human SNPs that indicate predicted effects on TF binding site affinities and are updated regularly via an automated process integrating a reference genome, a comprehensive SNP catalog, and a collection of PWMs.

Scientific Applications:

• Investigating regulatory variation: Enables analysis of how genetic variation alters TF binding landscapes in the human genome.
• SNP prioritization for functional studies: Supports selection of candidate SNPs that are predicted to disrupt, create, or change TF binding for downstream experimental validation.
• Annotation of SNP catalogs: Provides predicted TF binding impacts to aid interpretation of genetic variants in studies of gene regulation and disease associations.

Methodology:

Compares hg19 with alternate genomes incorporating 1000 Genomes Project variants (allele frequency ≥ 0.001), computes TF binding scores for reference and alternate sequences using PWMs, outputs text-file annotations indicating SNPs that abolish, create, or change TF binding, and updates the database via an automated process that integrates the reference genome, a comprehensive SNP catalog, and a collection of PWMs.