PT  - JOURNAL ARTICLE
AU  - Georg Stricker
AU  - Alexander Engelhardt
AU  - Daniel Schulz
AU  - Matthias Schmid
AU  - Achim Tresch
AU  - Julien Gagneur
TI  - Genome-wide generalized additive models
AID  - 10.1101/047464
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 047464
4099  - http://biorxiv.org/content/early/2016/04/06/047464.short
4100  - http://biorxiv.org/content/early/2016/04/06/047464.full
AB  - Chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) is a widely used approach to study protein-DNA interactions. To analyze ChIP-Seq data, practitioners are required to combine tools based on different statistical assumptions and dedicated to specific applications such as calling protein occupancy peaks or testing for differential occupancies. 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 estimated from the data eliminating ad-hoc binning and windowing needed by current approaches. We derived a peak caller based on GenoGAM with performance matching state-of-the-art methods. Moreover, GenoGAM provides significance testing for differential occupancy with controlled type I error rate and increased sensitivity over existing methods. By analyzing a set of DNA methylation data, we further demonstrate the potential of GenoGAM as a generic analysis tool for genome-wide assays.