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This package is for version 3.4 of Bioconductor; for the stable, up-to-date release version, see GenoGAM.

A GAM based framework for analysis of ChIP-Seq data

Bioconductor version: 3.4

This package allows statistical analysis of genome-wide data with smooth functions using generalized additive models based on the implementation from the R-package 'mgcv'. It provides methods for the statistical analysis of ChIP-Seq data including inference of protein occupancy, and pointwise and region-wise differential analysis. Estimation of dispersion and smoothing parameters is performed by cross-validation. Scaling of generalized additive model fitting to whole chromosomes is achieved by parallelization over overlapping genomic intervals.

Author: Georg Stricker [aut, cre], Alexander Engelhardt [aut], Julien Gagneur [aut]

Maintainer: Georg Stricker <georg.stricker at>

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PDF R Script GenoGAM: Genome-wide generalized additive models
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biocViews ChIPSeq, DifferentialExpression, DifferentialPeakCalling, Epigenetics, Genetics, Regression, Software
Version 1.2.1
In Bioconductor since BioC 3.3 (R-3.3) (1 year)
License GPL-2
Depends R (>= 3.3), Rsamtools(>= 1.18.2), SummarizedExperiment(>= 1.1.19), GenomicRanges(>= 1.23.16), methods
Imports BiocParallel(>= 1.5.17), data.table (>= 1.9.4), DESeq2(>= 1.11.23), futile.logger (>= 1.4.1), GenomeInfoDb(>= 1.7.6), GenomicAlignments(>= 1.7.17), IRanges(>= 2.5.30), mgcv (>= 1.8), reshape2 (>= 1.4.1), S4Vectors(>= 0.9.34), Biostrings(>= 2.39.14)
Suggests BiocStyle, chipseq(>= 1.21.2), LSD (>= 3.0.0), genefilter(>= 1.54.2), ggplot2 (>= 2.1.0), testthat, knitr
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