title: atSNP: transcription factor binding affinity testing for regulatory SNP detection.
abstract: BACKGROUND
  Genome-wide association studies revealed that most disease-associated single nucleotide polymorphisms (SNPs) are located in regulatory regions within introns or in regions between genes. Regulatory SNPs (rSNPs) are such SNPs that affect gene regulation by changing transcription factor (TF) binding affinities to genomic sequences. Identifying potential rSNPs is crucial for understanding disease mechanisms. In silico methods that evaluate the impact of SNPs on TF binding affinities are not scalable for large-scale analysis.
  
  
  RESULTS
  We describe A: ffinity T: esting for regulatory SNP: s (atSNP), a computationally efficient R package for identifying rSNPs in silico. atSNP implements an importance sampling algorithm coupled with a first-order Markov model for the background nucleotide sequences to test the significance of affinity scores and SNP-driven changes in these scores. Application of atSNP with >20 K SNPs indicates that atSNP is the only available tool for such a large-scale task. atSNP provides user-friendly output in the form of both tables and composite logo plots for visualizing SNP-motif interactions. Evaluations of atSNP with known rSNP-TF interactions indicate that SNP is able to prioritize motifs for a given set of SNPs with high accuracy.
  
  
  METHODS
  https://github.com/keleslab/atSNP.
  
  
  BACKGROUND
  keles@stat.wisc.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
authors.forename: Chandler
authors.initials: C
authors.lastname: Zuo
authors.affiliation: Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
authors.forename: Sunyoung
authors.initials: S
authors.lastname: Shin
authors.affiliation: Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
authors.forename: Sündüz
authors.initials: S
authors.lastname: Keleş
mesh_terms: D000465:Algorithms; D030541:Databases, Genetic; D005786:Gene Expression Regulation; D015894:Genome, Human; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods*; D006801:Humans; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D011485:Protein Binding; D012045:Regulatory Sequences, Nucleic Acid / Q000235:genetics*; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D014157:Transcription Factors