DOI: 10.18129/B9.bioc.GenoGAM    

This package is for version 3.9 of Bioconductor; for the stable, up-to-date release version, see GenoGAM.

A GAM based framework for analysis of ChIP-Seq data

Bioconductor version: 3.9

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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HTML R Script Modeling ChIP-Seq data with GenoGAM 2.0: A Genome-wide generalized additive model
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biocViews ChIPSeq, ChipOnChip, DifferentialExpression, DifferentialPeakCalling, Epigenetics, Genetics, ImmunoOncology, Regression, Software, WholeGenome
Version 2.2.0
In Bioconductor since BioC 3.3 (R-3.3) (3.5 years)
License GPL-2
Depends R (>= 3.5), SummarizedExperiment(>= 1.1.19), HDF5Array(>= 1.8.0), rhdf5(>= 2.21.6), S4Vectors(>= 0.9.34), Matrix (>= 1.2-8), data.table (>= 1.9.4)
Imports Rcpp (>= 0.12.14), sparseinv (>= 0.1.1), Rsamtools(>= 1.18.2), GenomicRanges(>= 1.23.16), BiocParallel(>= 1.5.17), DESeq2(>= 1.11.23), futile.logger (>= 1.4.1), GenomeInfoDb(>= 1.7.6), GenomicAlignments(>= 1.7.17), IRanges(>= 2.5.30), Biostrings(>= 2.39.14), DelayedArray(>= 0.3.19), methods, stats
LinkingTo Rcpp, RcppArmadillo
Suggests BiocStyle, chipseq(>= 1.21.2), LSD (>= 3.0.0), genefilter(>= 1.54.2), ggplot2 (>= 2.1.0), testthat, knitr, rmarkdown
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