title: NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data.
abstract: BACKGROUND
  Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) can locate transcription factor binding sites on genomic scale. Although many models and programs are available to call peaks, none has dominated its competition in comparison studies.
  
  
  RESULTS
  We propose a rigorous statistical model, the normal-exponential two-peak (NEXT-peak) model, which parallels the physical processes generating the empirical data, and which can naturally incorporate mappability information. The model therefore estimates total strength of binding (even if some binding locations do not map uniquely into a reference genome, effectively censoring them); it also assigns an error to an estimated binding location. The comparison study with existing programs on real ChIP-seq datasets (STAT1, NRSF, and ZNF143) demonstrates that the NEXT-peak model performs well both in calling peaks and locating them. The model also provides a goodness-of-fit test, to screen out spurious peaks and to infer multiple binding events in a region.
  
  
  CONCLUSIONS
  The NEXT-peak program calls peaks on any test dataset about as accurately as any other, but provides unusual accuracy in the estimated location of the peaks it calls. NEXT-peak is based on rigorous statistics, so its model also provides a principled foundation for a more elaborate statistical analysis of ChIP-seq data.
authors.affiliation: Mathematics and Statistics Department, Old Dominion University, Norfolk, VA 23529, USA. nxkim@odu.edu
authors.forename: Nak-Kyeong
authors.initials: NK
authors.lastname: Kim
authors.forename: Rasika V
authors.initials: RV
authors.lastname: Jayatillake
authors.forename: John L
authors.initials: JL
authors.lastname: Spouge
mesh_terms: D047369:Chromatin Immunoprecipitation / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D015233:Models, Statistical; D059372:Nucleotide Motifs; D012984:Software