title: GenoGAM: genome-wide generalized additive models for ChIP-Seq analysis.
abstract: BACKGROUND
  Chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) is a widely used approach to study protein-DNA interactions. Often, the quantities of interest are the differential occupancies relative to controls, between genetic backgrounds, treatments, or combinations thereof. Current methods for differential occupancy of ChIP-Seq data rely however on binning or sliding window techniques, for which the choice of the window and bin sizes are subjective.
  
  
  RESULTS
  Here, we present GenoGAM (Genome-wide Generalized Additive Model), which brings the well-established and flexible generalized additive models framework to genomic applications using a data parallelism strategy. We model ChIP-Seq read count frequencies as products of smooth functions along chromosomes. Smoothing parameters are objectively estimated from the data by cross-validation, eliminating ad hoc binning and windowing needed by current approaches. GenoGAM provides base-level and region-level significance testing for full factorial designs. Application to a ChIP-Seq dataset in yeast showed increased sensitivity over existing differential occupancy methods while controlling for type I error rate. By analyzing a set of DNA methylation data and illustrating an extension to a peak caller, we further demonstrate the potential of GenoGAM as a generic statistical modeling tool for genome-wide assays.
  
  
  METHODS
  Software is available from Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/GenoGAM.html .
  
  
  BACKGROUND
  gagneur@in.tum.de.
  
  
  BACKGROUND
  Supplementary information is available at Bioinformatics online.
authors.affiliation: Gene Center and Department of Biochemistry, Ludwig-Maximilians-Universität München, 80333 Munich, Germany.|Department of Informatics, Technische Universität München, 85748 Garching, Germany.
authors.forename: Georg
authors.initials: G
authors.lastname: Stricker
authors.affiliation: Gene Center and Department of Biochemistry, Ludwig-Maximilians-Universität München, 80333 Munich, Germany.
authors.forename: Alexander
authors.initials: A
authors.lastname: Engelhardt
authors.affiliation: Gene Center and Department of Biochemistry, Ludwig-Maximilians-Universität München, 80333 Munich, Germany.
authors.forename: Daniel
authors.initials: D
authors.lastname: Schulz
authors.affiliation: Institut für Medizinische Biometrie, Informatik und Epidemiologie, University Hospital Bonn, 53105 Bonn, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Schmid
authors.affiliation: Institute for Genetics, University of Cologne, 50647 Cologne, Germany.
authors.forename: Achim
authors.initials: A
authors.lastname: Tresch
authors.affiliation: Gene Center and Department of Biochemistry, Ludwig-Maximilians-Universität München, 80333 Munich, Germany.|Department of Informatics, Technische Universität München, 85748 Garching, Germany.
authors.forename: Julien
authors.initials: J
authors.lastname: Gagneur
mesh_terms: D000818:Animals; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D019175:DNA Methylation; D023281:Genomics / Q000379:methods; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D051379:Mice; D008954:Models, Biological; D015233:Models, Statistical; D017422:Sequence Analysis, DNA / Q000379:methods; D012984:Software; D015003:Yeasts / Q000235:genetics