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Related Concept Videos

Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Updated: Apr 9, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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atSNP: transcription factor binding affinity testing for regulatory SNP detection.

Chandler Zuo1, Sunyoung Shin1, Sündüz Keleş1

  • 1Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.

Bioinformatics (Oxford, England)
|June 21, 2015
PubMed
Summary

We developed atSNP, an efficient R package to identify regulatory SNPs (rSNPs) by analyzing their impact on transcription factor binding. This tool enables large-scale analysis of disease-associated genetic variations.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Most disease-associated single nucleotide polymorphisms (SNPs) are found in gene regulatory regions.
  • Regulatory SNPs (rSNPs) influence gene regulation by altering transcription factor (TF) binding.
  • Current in silico methods struggle with the scalability required for large-scale SNP analysis.

Purpose of the Study:

  • To introduce atSNP, a computationally efficient R package for identifying rSNPs.
  • To provide a scalable in silico tool for analyzing the impact of SNPs on TF binding affinities.
  • To facilitate the identification of disease-associated rSNPs.

Main Methods:

  • atSNP utilizes an importance sampling algorithm.
  • A first-order Markov model is employed for background nucleotide sequences.
  • The package tests the significance of affinity scores and SNP-induced changes.

Main Results:

  • atSNP is capable of handling large-scale analyses with over 20,000 SNPs.
  • The package offers user-friendly outputs including tables and composite logo plots.
  • atSNP accurately prioritizes motifs for given SNPs, demonstrating high accuracy in identifying known rSNP-TF interactions.

Conclusions:

  • atSNP is a scalable and accurate tool for identifying regulatory SNPs.
  • The R package provides efficient in silico analysis for large datasets.
  • atSNP aids in understanding disease mechanisms by identifying potential rSNPs and their TF interactions.