recipe bioconductor-stan

Genome segmentation with hidden Markov models has become a useful tool to annotate genomic elements, such as promoters and enhancers. STAN (genomic STate ANnotation) implements (bidirectional) hidden Markov models (HMMs) using a variety of different probability distributions, which can model a wide range of current genomic data (e.g. continuous, discrete, binary). STAN de novo learns and annotates the genome into a given number of ‘genomic states’. The ‘genomic states’ may for instance reflect distinct genome-associated protein complexes (e.g. ‘transcription states’) or describe recurring patterns of chromatin features (referred to as ‘chromatin states’). Unlike other tools, STAN also allows for the integration of strand-specific (e.g. RNA) and non-strand-specific data (e.g. ChIP).

Homepage

https://bioconductor.org/packages/3.9/bioc/html/STAN.html

License

GPL (>= 2)

Recipe

/bioconductor-stan/meta.yaml

Links

biotools: stan

package bioconductor-stan

(downloads) docker_bioconductor-stan

Versions

2.10.0-0, 2.8.0-0, 2.6.0-0

Depends bioconductor-biocgenerics

>=0.28.0,<0.29.0

Depends bioconductor-genomeinfodb

>=1.18.0,<1.19.0

Depends bioconductor-genomicranges

>=1.34.0,<1.35.0

Depends bioconductor-gviz

>=1.26.0,<1.27.0

Depends bioconductor-iranges

>=2.16.0,<2.17.0

Depends bioconductor-s4vectors

>=0.20.0,<0.21.0

Depends libgcc-ng

>=7.3.0

Depends libstdcxx-ng

>=7.3.0

Depends r-base

>=3.5.1,<3.5.2.0a0

Depends r-poilog

Depends r-rsolnp

Requirements

Installation

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

conda install bioconductor-stan

and update with:

conda update bioconductor-stan

or use the docker container:

docker pull quay.io/biocontainers/bioconductor-stan:<tag>

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