title: JAMIE: joint analysis of multiple ChIP-chip experiments.
abstract: BACKGROUND
  Chromatin immunoprecipitation followed by genome tiling array hybridization (ChIP-chip) is a powerful approach to identify transcription factor binding sites (TFBSs) in target genomes. When multiple related ChIP-chip datasets are available, analyzing them jointly allows one to borrow information across datasets to improve peak detection. This is particularly useful for analyzing noisy datasets.
  
  
  RESULTS
  We propose a hierarchical mixture model and develop an R package JAMIE to perform the joint analysis. The genome is assumed to consist of background and potential binding regions (PBRs). PBRs have context-dependent probabilities to become bona fide binding sites in individual datasets. This model captures the correlation among datasets, which provides basis for sharing information across experiments. Real data tests illustrate the advantage of JAMIE over a strategy that analyzes individual datasets separately.
  
  
  BACKGROUND
  JAMIE is freely available from http://www.biostat.jhsph.edu/~hji/jamie
authors.affiliation: Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 North Wolfe Street, Baltimore, MD 21205, USA.
authors.forename: Hao
authors.initials: H
authors.lastname: Wu
authors.forename: Hongkai
authors.initials: H
authors.lastname: Ji
mesh_terms: D001665:Binding Sites; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D016678:Genome / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011485:Protein Binding