SEMpl

SEMpl predicts the effects of single-nucleotide polymorphisms (SNPs) on transcription factor binding affinity by leveraging genome-wide ChIP-seq signal intensity at SNP sites within transcription factor binding sites and cataloging the effects of all possible mutations within TFBS motifs to prioritize noncoding variants for functional study.

Key Features:

• Transcription Factor Binding Affinity Estimation: Estimates SNP-induced changes in transcription factor binding by analyzing ChIP-seq signal intensity at SNP sites within functional transcription factor binding sites (TFBSs) genome-wide.
• Comprehensive Mutation Cataloging: Systematically catalogs the effects of every possible mutation within TFBS motifs to predict how SNPs may alter transcription factor binding.
• Identification of Disease-Causing Loci: Predicts changes in transcription factor binding that can be used to prioritize regulatory loci and candidate noncoding SNPs for further functional investigation.

Scientific Applications:

• Functional Genomics: Investigates how noncoding SNPs influence gene regulation and contribute to complex diseases.
• Regulatory Loci Discovery: Identifies potential regulatory elements implicated in disease pathogenesis by predicting altered transcription factor binding.
• Prioritization of Noncoding Variants: Assists in selecting candidate SNPs for experimental validation based on predicted effects on transcription factor binding.

Methodology:

SEMpl leverages genome-wide ChIP-seq signal intensity at SNP sites within transcription factor binding sites and systematically catalogs the effects of all possible mutations within TFBS motifs.