recipe bioconductor-genogam

A GAM based framework for analysis of ChIP-Seq data

Homepage

https://bioconductor.org/packages/3.14/bioc/html/GenoGAM.html

License

GPL-2

Recipe

/bioconductor-genogam/meta.yaml

Links

biotools: genogam, doi: 10.1093/bioinformatics/btx150

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.

package bioconductor-genogam

Versions

2.11.0-0,  2.10.0-0,  2.8.0-1,  2.8.0-0,  2.6.0-0,  2.4.0-0,  2.2.0-1,  2.0.2-0,  1.8.0-0, 

2.11.0-0,  2.10.0-0,  2.8.0-1,  2.8.0-0,  2.6.0-0,  2.4.0-0,  2.2.0-1,  2.0.2-0,  1.8.0-0,  1.6.0-0

Depends

• bioconductor-biocparallel >=1.28.0,<1.29.0

• bioconductor-biostrings >=2.62.0,<2.63.0

• bioconductor-delayedarray >=0.20.0,<0.21.0

• bioconductor-deseq2 >=1.34.0,<1.35.0

• bioconductor-genomeinfodb >=1.30.0,<1.31.0

• bioconductor-genomicalignments >=1.30.0,<1.31.0

• bioconductor-genomicranges >=1.46.0,<1.47.0

• bioconductor-hdf5array >=1.22.0,<1.23.0

• bioconductor-iranges >=2.28.0,<2.29.0

• bioconductor-rhdf5 >=2.38.0,<2.39.0

• bioconductor-rsamtools >=2.10.0,<2.11.0

• bioconductor-s4vectors >=0.32.0,<0.33.0

• bioconductor-summarizedexperiment >=1.24.0,<1.25.0

• libblas >=3.8.0,<4.0a0

• libgcc-ng >=9.4.0

• liblapack >=3.8.0,<4.0a0

• libstdcxx-ng >=9.4.0

• r-base >=4.1,<4.2.0a0

• r-data.table >=1.9.4

• r-futile.logger >=1.4.1

• r-matrix >=1.2-8

• r-rcpp >=0.12.14

• r-rcpparmadillo

• r-sparseinv >=0.1.1

Required By

Installation

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 quay.io/biocontainers/bioconductor-genogam:<tag>

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

Download stats

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