Genome-wide generalized additive models

13 Chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) is a widely used 14 approach to study protein-DNA interactions. To analyze ChIP-Seq data, practitioners are 15 required to combine tools based on different statistical assumptions and dedicated to spe16 cific applications such as calling protein occupancy peaks or testing for differential occu17 pancies. Here, we present GenoGAM (Genome-wide Generalized Additive Model), which 18 brings the well-established and flexible generalized additive models framework to genomic 19 applications using a data parallelism strategy. We model ChIP-Seq read count frequencies 20 as products of smooth functions along chromosomes. Smoothing parameters are estimated 21 from the data eliminating ad-hoc binning and windowing needed by current approaches. 22 We derived a peak caller based on GenoGAM with performance matching state-of-the-art 23 methods. Moreover, GenoGAM provides significance testing for differential occupancy with 24 1 . CC-BY 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/047464 doi: bioRxiv preprint first posted online Apr. 6, 2016;