Apr 6, 2016

Genome-wide generalized additive models

BioRxiv : the Preprint Server for Biology
Georg StrickerJulien Gagneur

Abstract

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.

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Mentioned in this Paper

Genome-Wide Association Study
DNA Methylation [PE]
Genome
DNA Methylation
Chromatin Immunoprecipitation
Occupational Health Services
Protein-Protein Interaction
Genomics
Deep Sequencing
Chromosomes

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