Name
Abstract | ||
|---|---|---|
Motivation: 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1093/bioinformatics/btx150 | BIOINFORMATICS |
Field | DocType | Volume |
Sliding window protocol,Bin,Chip,Data parallelism,Smoothing,Statistical model,Type I and type II errors,Bioinformatics,Generalized additive model,Mathematics | Journal | 33 |
Issue | ISSN | Citations |
15 | 1367-4803 | 1 |
PageRank | References | Authors |
0.63 | 4 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Georg Stricker | 1 | 1 | 0.63 |
| Alexander Engelhardt | 2 | 1 | 0.96 |
| Daniel Schulz | 3 | 1 | 0.63 |
| Matthias Schmid | 4 | 28 | 6.84 |
| Achim Tresch | 5 | 3 | 1.47 |
| Julien Gagneur | 6 | 3 | 1.67 |