title: Nucleosome positioning from tiling microarray data.
abstract: BACKGROUND
  The packaging of DNA around nucleosomes in eukaryotic cells plays a crucial role in regulation of gene expression, and other DNA-related processes. To better understand the regulatory role of nucleosomes, it is important to pinpoint their position in a high (5-10 bp) resolution. Toward this end, several recent works used dense tiling arrays to map nucleosomes in a high-throughput manner. These data were then parsed and hand-curated, and the positions of nucleosomes were assessed.
  
  
  RESULTS
  In this manuscript, we present a fully automated algorithm to analyze such data and predict the exact location of nucleosomes. We introduce a method, based on a probabilistic graphical model, to increase the resolution of our predictions even beyond that of the microarray used. We show how to build such a model and how to compile it into a simple Hidden Markov Model, allowing for a fast and accurate inference of nucleosome positions. We applied our model to nucleosomal data from mid-log yeast cells reported by Yuan et al. and compared our predictions to those of the original paper; to a more recent method that uses five times denser tiling arrays as explained by Lee et al.; and to a curated set of literature-based nucleosome positions. Our results suggest that by applying our algorithm to the same data used by Yuan et al. our fully automated model traced 13% more nucleosomes, and increased the overall accuracy by about 20%. We believe that such an improvement opens the way for a better understanding of the regulatory mechanisms controlling gene expression, and how they are encoded in the DNA.
authors.affiliation: School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel.
authors.forename: Moran
authors.initials: M
authors.lastname: Yassour
authors.forename: Tommy
authors.initials: T
authors.lastname: Kaplan
authors.forename: Ariel
authors.initials: A
authors.lastname: Jaimovich
authors.forename: Nir
authors.initials: N
authors.lastname: Friedman
mesh_terms: D001185:Artificial Intelligence; D001483:Base Sequence; D008390:Markov Chains; D008957:Models, Genetic; D008969:Molecular Sequence Data; D009707:Nucleosomes / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D009707:Nucleosomes