recipe bioconductor-genogam

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.







biotools: genogam, doi: 10.1093/bioinformatics/btx150

package bioconductor-genogam

(downloads) docker_bioconductor-genogam


2.0.2-0, 1.8.0-0, 1.6.0-0

Depends bioconductor-biocparallel


Depends bioconductor-biostrings


Depends bioconductor-delayedarray


Depends bioconductor-deseq2


Depends bioconductor-genomeinfodb


Depends bioconductor-genomicalignments


Depends bioconductor-genomicranges


Depends bioconductor-hdf5array


Depends bioconductor-iranges


Depends bioconductor-rhdf5


Depends bioconductor-rsamtools


Depends bioconductor-s4vectors


Depends bioconductor-summarizedexperiment


Depends libgcc-ng


Depends libstdcxx-ng


Depends r-base


Depends r-data.table


Depends r-futile.logger


Depends r-matrix


Depends r-rcpp


Depends r-rcpparmadillo

Depends r-sparseinv




With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-genogam

and update with:

conda update bioconductor-genogam

or use the docker container:

docker pull<tag>

(see bioconductor-genogam/tags for valid values for <tag>)